47:"/_next/static/chunks/2g8w-80ml0qgh.js" 48:"/_next/static/chunks/0iv56yrji_20j.js" 49:"/_next/static/chunks/26v-2o8eqscfn.js" 4a:"/_next/static/chunks/0lc44eilyyvky.js" 4b:"/_next/static/chunks/1jl6tco-uuy-j.js" 4c:"/_next/static/chunks/1ot1rbfa0d_n4.js" 4d:"/_next/static/chunks/3n7h0f3nk8y8p.js" 4e:"/_next/static/chunks/3k2gyg36fnag_.js" 4f:"/_next/static/chunks/3m0r7x0jt5wm7.js" 50:"/_next/static/chunks/2fohwszm1xz1m.js" 51:"/_next/static/chunks/3bgoyiso1rhn7.js" 52:"/_next/static/chunks/19bc9syhv3w28.js" 53:"/_next/static/chunks/0wa9m1-tphf9u.js" 54:"/_next/static/chunks/0m2d8jznc9t02.js" 55:"/_next/static/chunks/1xu72k8i0p_ma.js" 56:"/_next/static/chunks/2a1rt036gjwtj.js" 57:"/_next/static/chunks/1s05-oinjbm85.js" 58:"/_next/static/chunks/20k3408cq3fwt.js" 59:"/_next/static/chunks/1_lo_qve3gu86.js" 5a:"/_next/static/chunks/3di53f0fz7pw_.js" 5b:"/_next/static/chunks/3m1xjj5kw5fie.js" 5c:"/_next/static/chunks/0so1i35v7xcta.js" 5d:"/_next/static/chunks/082jo-0ajazgr.js" 5e:"/_next/static/chunks/0-a64pg0hn7g7.js" 5f:"/_next/static/chunks/09nq57stwzkgf.js" 60:"/_next/static/chunks/20329i6m_04t6.js" 61:"/_next/static/chunks/0i3-eynrkvqi7.js" 62:"/_next/static/chunks/0oag6lffx7d01.js" 63:"/_next/static/chunks/1nyz-fz1mk3y6.js" 64:"/_next/static/chunks/1dl4k5870muic.js" 65:"/_next/static/chunks/2ozcrjb_hh74t.js" 66:"/_next/static/chunks/18ggtgz2yf759.js" 67:"/_next/static/chunks/1tn-0fivsxsv1.js" 68:"/_next/static/chunks/0hlb44ca7-4a2.js" 69:"/_next/static/chunks/1ch_-e7_eco00.js" 6a:"/_next/static/chunks/40mq7ggq-ozo4.js" 6b:"/_next/static/chunks/2112y_x317i7a.js" 6c:"/_next/static/chunks/2k8pepwc4pxns.js" 6d:"/_next/static/chunks/2hr13i-t0krnb.js" 6e:"/_next/static/chunks/0hy4a31wjpabs.js" 6f:"/_next/static/chunks/2mw79vtbi206x.js" 70:"/_next/static/chunks/253gnmj0-cj17.js" 71:I[593304,["$2d","$2e","$2f","$30","$31","$32","$47","$48","$49","$4a","$4b","$4c","$4d","$4e","$4f","$50","$51","$52","$53","$54","$55","$56","$57","$58","$59","$5a","$5b","$5c","$5d","$5e","$5f","$60","$61","$62","$63","$64","$65","$66","$67","$68","$69","$6a","$6b","$6c","$6d","$6e","$6f","$70"],"OwnedHydration"] 72:T6d1,Not every problem should be solved with machine learning. Before committing to an ML approach, rigorous feasibility assessment prevents wasted effort and misallocated resources. The assessment examines four dimensions: **data feasibility**, **technical feasibility**, **organizational feasibility**, and **economic feasibility**. **Data Feasibility:** The fundamental question: *Does the signal necessary to solve this problem exist in available data?* This assessment requires examining: 1. **Label availability** — Supervised learning requires labels. Are they available? At what cost? With what accuracy? 2. **Signal-to-noise ratio** — Does the data contain predictive information? Sometimes the task is fundamentally unpredictable. 3. **Data volume** — Is there sufficient data for the complexity of the task? Deep learning on 500 samples is wishful thinking. 4. **Representation** — Does the data represent the deployment distribution? Training on US data for global deployment fails. 5. **Temporal stability** — Does the relationship between features and outcomes persist? Market data from 2019 may not predict 2024. **Technical Feasibility:** Even with perfect data, technical constraints may make the problem intractable: 1. **Latency requirements** — Sub-10ms inference eliminates most large models. 2. **Hardware constraints** — Edge deployment restricts model size and computational requirements. 3. **Explainability requirements** — Regulated domains may preclude black-box models entirely. 4. **Privacy constraints** — Differential privacy, federated learning, or on-device processing may be mandatory. 5. **Accuracy floor** — Some problems require 99%+ accuracy; others tolerate 70%. Know which you face.73:T452,**Organizational Feasibility:** Technical solutions require organizational support: 1. **Stakeholder commitment** — Does leadership support a multi-month investment with uncertain outcomes? 2. **Integration path** — Can the model be integrated into existing systems and workflows? 3. **Maintenance capacity** — Who will monitor, retrain, and maintain the model post-deployment? 4. **Change management** — Will end users adopt the system? Resistance undermines even perfect models. 5. **Regulatory approval** — In regulated industries, model approval may take longer than development. **Economic Feasibility:** The final arbiter—does the ROI justify the investment? - **Development cost** — Personnel time, infrastructure, licensing - **Operational cost** — Inference compute, monitoring, maintenance - **Opportunity cost** — What else could this team accomplish? - **Value delivered** — Revenue increase, cost reduction, risk mitigation A 2% accuracy improvement that costs $500K to achieve and delivers $50K annual value is a poor investment. Always quantify the expected impact.74:T1562,# Problem Formulation Template # A rigorous specification that forces clarity from dataclasses import dataclass from typing import List, Dict, Optional from enum import Enum class TaskType(Enum): BINARY_CLASSIFICATION = "binary_classification" MULTICLASS_CLASSIFICATION = "multiclass_classification" REGRESSION = "regression" RANKING = "ranking" SEQUENCE_LABELING = "sequence_labeling" GENERATION = "generation" CLUSTERING = "clustering" ANOMALY_DETECTION = "anomaly_detection" @dataclass class MLProblemFormulation: """ Comprehensive problem formulation document. Forces explicit consideration of all critical dimensions. """ # Business Context business_objective: str # What business outcome is targeted? success_definition: str # How will success be measured in business terms? stakeholders: List[str] # Who are the decision-makers? # Task Specification task_type: TaskType input_features: List[str] # Features available at inference inference_time_constraints: List[str] # What cannot be computed in real-time output_specification: str # Exact output format and semantics # Data Specification training_data_source: str label_definition: str # Precise definition of positive/negative or target label_acquisition_method: str # How are labels obtained? expected_data_volume: int data_freshness_requirement: str # How recent must training data be? # Constraints latency_requirement_ms: Optional[int] minimum_acceptable_performance: Dict[str, float] # metric: threshold explainability_requirement: str # None, global, local, regulatory privacy_constraints: List[str] # Evaluation primary_metric: str secondary_metrics: List[str] evaluation_methodology: str # Cross-validation, temporal split, etc. offline_online_gap_risk: str # Known risks of offline-online mismatch def validate(self) -> List[str]: """ Validates completeness of problem formulation. Returns list of issues that require resolution. """ issues = [] if not self.label_definition: issues.append("Label definition is missing - critical for alignment") if not self.minimum_acceptable_performance: issues.append("No minimum performance threshold defined") if self.task_type == TaskType.BINARY_CLASSIFICATION: if "precision" not in self.primary_metric.lower() and \ "recall" not in self.primary_metric.lower(): issues.append( "Binary classification without precision/recall " "consideration - error costs may be unbalanced" ) if not self.offline_online_gap_risk: issues.append( "Offline-online gap risk not assessed - " "distribution shift may invalidate offline metrics" ) return issues # Example: Churn Prediction Problem Formulation churn_formulation = MLProblemFormulation( # Business Context business_objective="Reduce voluntary subscription cancellations by identifying at-risk users for proactive intervention", success_definition="20% reduction in monthly churn rate among users targeted by the retention campaign", stakeholders=["Product (user experience)", "Marketing (retention campaigns)", "Finance (revenue impact)"], # Task Specification task_type=TaskType.BINARY_CLASSIFICATION, input_features=[ "days_since_last_active", "weekly_session_count_4w", "feature_adoption_score", "support_ticket_count_30d", "subscription_tenure_months", "payment_method_risk_score" ], inference_time_constraints=["LTV prediction (requires batch computation)"], output_specification="Probability of churn within next 30 days, calibrated [0,1]", # Data Specification training_data_source="User activity logs + subscription events", label_definition="User initiated cancellation or failed renewal within 30-day window after prediction point", label_acquisition_method="Historical observation with 30-day wait period", expected_data_volume=500000, # labeled examples data_freshness_requirement="Training data no older than 90 days due to product changes", # Constraints latency_requirement_ms=50, # Batch scoring acceptable minimum_acceptable_performance={ "precision_at_10pct_recall": 0.30, # Among users we target, 30%+ actually would churn "auc_roc": 0.75 }, explainability_requirement="Local explanations for customer support use case", privacy_constraints=["No direct access to message content", "GDPR right-to-explanation"], # Evaluation primary_metric="Precision at 10% recall (matches intervention capacity)", secondary_metrics=["AUC-ROC", "Calibration error", "Lift at top decile"], evaluation_methodology="Temporal train-test split (train < July, test = July-August)", offline_online_gap_risk="Users receiving retention offers may behave differently than historical patterns" ) # Validate the formulation issues = churn_formulation.validate() if issues: print("Formulation issues to resolve:") for issue in issues: print(f" - {issue}") else: print("Problem formulation complete and validated")75:T450,A well-scoped ML problem has explicit boundaries—what is included and what is deliberately excluded. Without boundaries, scope creep transforms a deliverable project into an endless research odyssey. **Defining What's In Scope:** 1. **User segments** — Which users does this model serve? All users? Specific tiers? Specific geographies? 2. **Use cases** — Which scenarios is the model designed for? Which edge cases are explicitly excluded? 3. **Input domains** — What input space is the model expected to handle? What constitutes out-of-distribution? 4. **Performance envelope** — Under what conditions is the model expected to meet quality thresholds? **Defining What's Out of Scope:** Explicitly listing exclusions prevents misunderstandings: - *"The model does not handle new users with <7 days of history (cold start excluded from V1)"* - *"Enterprise customers with custom pricing are excluded from the churn model"* - *"Non-English queries are out of scope for this release"* **Constraint Documentation:** Every ML system operates within constraints that shape the solution space:76:T4cc,**Minimum Viable Model (MVM):** Just as products have MVPs, ML projects should define an MVM—the simplest model that delivers value. The MVM serves multiple purposes: 1. **Validation checkpoint** — Proves the problem is solvable before heavy investment 2. **Baseline establishment** — Sets a performance floor for more complex approaches 3. **Integration testing** — Exercises the deployment pipeline with a real model 4. **Stakeholder alignment** — Demonstrates what "good" looks like in practice The MVM should be: - Buildable in 1-2 weeks - Uses readily available data - Employs established techniques (logistic regression, gradient boosting) - Deployable through production infrastructure **Example MVM progression:** ``` MVM: Logistic regression on 5 hand-crafted features → Establishes feasibility and baseline metrics V1: Gradient boosted trees on 50 engineered features → Improves performance, validates feature value V2: Neural network with embeddings → Pushes performance ceiling, requires more infrastructure V3: Ensemble with online learning → Addresses distribution shift, production-grade ``` Each increment should have explicit performance targets that justify the added complexity.77:T1222,from dataclasses import dataclass from typing import Dict, List, Callable import numpy as np @dataclass class MetricDefinition: """Complete specification of an evaluation metric.""" name: str description: str formula: str # LaTeX or prose explanation compute_fn: Callable # Actual computation higher_is_better: bool requires_probabilities: bool threshold_dependent: bool business_interpretation: str @dataclass class SuccessCriteria: """ Comprehensive success criteria for an ML project. Separates model-level from business-level metrics. """ # Model-level success (offline metrics) primary_model_metric: MetricDefinition primary_threshold: float secondary_model_metrics: Dict[str, float] # metric_name: minimum_threshold # Business-level success (online metrics) primary_business_metric: str business_calculation: str # How model performance translates to business value expected_business_impact: str # Guardrail constraints (must not violate) guardrails: Dict[str, float] # metric: maximum_acceptable_violation # Statistical significance requirements minimum_sample_size: int # For A/B testing confidence_level: float minimum_detectable_effect: float def generate_success_document(self) -> str: """ Generates a stakeholder-readable success criteria document. """ doc = f""" MODEL SUCCESS CRITERIA ====================== PRIMARY METRIC: {self.primary_model_metric.name} Threshold for success: {self.primary_model_metric.name} >= {self.primary_threshold} Business interpretation: {self.primary_model_metric.business_interpretation} SECONDARY METRICS (must also meet): """ for metric, threshold in self.secondary_model_metrics.items(): doc += f" - {metric} >= {threshold} " doc += f""" GUARDRAILS (must not violate): """ for metric, max_violation in self.guardrails.items(): doc += f" - {metric}: maximum acceptable = {max_violation} " doc += f""" BUSINESS IMPACT: Primary business metric: {self.primary_business_metric} Calculation: {self.business_calculation} Expected impact: {self.expected_business_impact} A/B TEST REQUIREMENTS: - Minimum sample size: {self.minimum_sample_size:,} - Confidence level: {self.confidence_level * 100}% - Minimum detectable effect: {self.minimum_detectable_effect * 100}% """ return doc # Example: Fraud Detection Success Criteria def compute_precision_at_recall(y_true, y_prob, target_recall=0.80): """Compute precision at a fixed recall threshold.""" from sklearn.metrics import precision_recall_curve precision, recall, thresholds = precision_recall_curve(y_true, y_prob) # Find threshold that achieves target recall valid_idx = np.where(recall >= target_recall)[0] if len(valid_idx) == 0: return 0.0 best_idx = valid_idx[np.argmax(precision[valid_idx])] return precision[best_idx] fraud_success_criteria = SuccessCriteria( # Model metrics primary_model_metric=MetricDefinition( name="Precision at 80% Recall", description="Among all predicted fraud cases at the threshold that catches 80% of actual fraud, what fraction are truly fraudulent?", formula="P(fraud | predicted_fraud) when threshold set to achieve R(fraud) = 0.80", compute_fn=compute_precision_at_recall, higher_is_better=True, requires_probabilities=True, threshold_dependent=True, business_interpretation="At our manual review capacity, 50%+ of flagged transactions should be actual fraud to justify investigation cost" ), primary_threshold=0.50, # 50% precision at 80% recall secondary_model_metrics={ "auc_roc": 0.90, "calibration_error": 0.05, # Maximum expected calibration error }, # Business metrics primary_business_metric="Net Fraud Loss Reduction", business_calculation="(Caught fraud - False positive cost) - (Model operation cost)", expected_business_impact="$2M annual reduction in fraud losses; $200K review efficiency gain", # Guardrails guardrails={ "customer_friction_rate": 0.02, # No more than 2% of legitimate transactions flagged "review_queue_time_hours": 4, # Fraud queue must clear within 4 hours }, # A/B test requirements minimum_sample_size=50000, # transactions per variant confidence_level=0.95, minimum_detectable_effect=0.10 # 10% relative improvement ) print(fraud_success_criteria.generate_success_document())78:T4a1,Experience reveals recurring patterns of scoping failures. Understanding these anti-patterns protects against repeating common mistakes. **Pitfall 1: Solution Searching for a Problem** *"We want to use deep learning somewhere. Find a use case."* Technology-first thinking inverts the correct order. When the goal is to deploy a particular technique rather than solve a business problem, the result is: - Forced applications that don't fit the problem structure - Overengineered solutions that simple methods would handle - Stakeholder frustration when the technology doesn't deliver expected magic **Correction:** Always start with the business problem. The technique should be selected to serve the problem, not the reverse. **Pitfall 2: Scope Creep by Committee** *"Can we also add X? What about Y? Everyone wants Z."* Well-intentioned stakeholders continuously add requirements until the project becomes impossible. Each addition seems minor but the total becomes undeliverable. **Correction:** Freeze scope after the scoping phase. New requirements go to V2. If something truly must be added, something else must be removed. Zero-sum requirement changes enforce discipline.79:T45a,**Pitfall 3: The Single Metric Obsession** *"Just optimize for accuracy. That's all that matters."* Real systems have multiple objectives that trade off against each other. A spam filter optimized purely for precision catches no spam; optimized purely for recall, it blocks legitimate email. Single-metric optimization ignores constraints and produces unusable systems. **Correction:** Define a primary metric for optimization but add guardrail constraints that cannot be violated. The objective becomes: maximize primary metric subject to constraint satisfaction. **Pitfall 4: Ignoring the Human Loop** ML systems rarely operate autonomously. They typically augment human decision-making. Scoping that ignores the human interface produces: - Outputs that humans can't interpret or action - Workflows that create more work instead of reducing it - User resistance that undermines adoption **Correction:** Map the human workflow explicitly. Where does the model output enter? Who acts on it? What format do they need? What happens when confidence is low? Design the model to serve the human, not replace them.7a:T56c,A formal scoping document serves as the contract between technical teams and stakeholders. It captures decisions, assumptions, and commitments that govern the project. The following template synthesizes best practices from production ML teams: **Document Structure:** 1. **Executive Summary** (1/2 page) - Business problem and ML approach summary - Expected impact and investment required - Key risks and dependencies 2. **Business Context** (1 page) - Problem statement and business motivation - Current solution and its limitations - Stakeholders and their requirements - Success criteria in business terms 3. **Technical Specification** (2-3 pages) - ML task formulation - Input features and output specification - Data sources and label definition - Model performance requirements - Infrastructure constraints 4. **Scope Definition** (1 page) - In-scope use cases and user segments - Out-of-scope exclusions - Assumptions and dependencies - Known limitations 5. **Risk Assessment** (1 page) - Technical risks and mitigations - Data risks (availability, quality, shift) - Organizational risks (adoption, maintenance) - Ethical and fairness considerations 6. **Implementation Plan** (1 page) - Phases and milestones - Resource requirements - Timeline with decision checkpoints - Success gates for each phase7b:T13a7,# ML Project Scoping Document ## Project: Customer Churn Prediction v1.0 **Date:** 2024-01-15 **Author:** ML Team Lead **Stakeholders:** Product, Marketing, Finance **Status:** APPROVED --- ## 1. Executive Summary We propose building a machine learning model to predict customer churn 30 days in advance, enabling proactive retention interventions. **Expected Impact:** 15-25% reduction in monthly churn among targeted users **Investment:** 3 months engineering, $50K infrastructure **Key Risk:** Label definition depends on unreliable cancellation tracking --- ## 2. Business Context ### Problem Statement Monthly churn rate of 5.2% costs approximately $12M annually in lost recurring revenue. Current retention efforts are reactive (after cancellation) rather than proactive (preventing cancellation). ### Current Solution Manual identification based on support tickets and engagement drops. Coverage: ~10% of churning users. Success rate: ~15% prevention. ### Stakeholder Requirements | Stakeholder | Requirement | Priority | |-------------|-------------|----------| | Product | Minimize false alerts to users | P0 | | Marketing | Actionable segments for campaigns | P1 | | Finance | ROI-positive intervention program | P0 | ### Business Success Criteria - 20% relative reduction in monthly churn rate - < 2% of retained users report negative survey response to outreach - Positive ROI within 6 months of deployment --- ## 3. Technical Specification ### ML Task Formulation **Type:** Binary Classification **Input:** User activity features over trailing 28-day window **Output:** Calibrated probability of churn in next 30 days **Threshold:** Set to achieve 15% recall (matching intervention capacity) ### Feature Specification | Feature Category | Examples | Availability | |------------------|----------|--------------| | Engagement | Sessions, time spent, features used | Real-time | | Subscription | Tenure, plan type, payment history | Real-time | | Support | Tickets, satisfaction scores | 24-hour lag | | Social | Team size, invitations sent | Real-time | ### Label Definition **Positive (Churn):** User-initiated cancellation OR failed renewal not recovered within 7 days, occurring 1-30 days after prediction point. **Negative (Retained):** Active subscription 30+ days after prediction. **Exclusions:** Enterprise customers, trial users, accounts < 14 days old. ### Performance Requirements | Metric | Minimum | Target | |--------|---------|--------| | Precision @ 15% Recall | 30% | 50% | | AUC-ROC | 0.75 | 0.85 | | Calibration Error (ECE) | < 0.10 | < 0.05 | ### Infrastructure Constraints - Inference latency: < 100ms (batch acceptable) - Model size: < 500MB (MLflow deployment) - Retraining: Weekly automated pipeline --- ## 4. Scope Definition ### In Scope (v1.0) - Self-serve subscription customers - US and EU regions - Voluntary churn only (excludes payment failures) ### Out of Scope (v1.0) - Enterprise/custom pricing customers - Free tier users - Involuntary churn (payment issues handled separately) - New users < 14 days (cold start problem deferred to v2) ### Assumptions 1. Marketing team has capacity to act on 15% of user base weekly 2. Historical cancellation data accurately reflects churn patterns 3. Product will not undergo major changes during development ### Known Limitations - No visibility into external factors (competitor offers, life changes) - Cannot predict enterprise churn (insufficient volume for training) --- ## 5. Risk Assessment | Risk | Probability | Impact | Mitigation | |------|-------------|--------|------------| | Insufficient signal in features | Medium | High | MVM phase to validate feasibility | | Label noise from data quality | High | Medium | Manual audit of label sample | | Distribution shift post-launch | High | Medium | Weekly retraining, drift monitoring | | Low adoption by marketing | Medium | High | Co-design intervention workflow | | Privacy/GDPR challenges | Low | High | Legal review before feature selection | ### Ethical Considerations - Model must not discriminate based on protected attributes - Fairness audit across user segments before deployment - Transparency: users may request explanation for targeting --- ## 6. Implementation Plan ### Phase 1: Feasibility (Weeks 1-3) - Data extraction and label audit - MVM baseline (logistic regression) - **Gate:** AUC > 0.70 OR project descoped ### Phase 2: Development (Weeks 4-8) - Feature engineering - Model experimentation - Offline evaluation - **Gate:** Meets minimum performance thresholds ### Phase 3: Integration (Weeks 9-10) - Scoring pipeline deployment - Integration with marketing tools - Shadow mode operation ### Phase 4: Launch (Weeks 11-12) - A/B test design and execution - Monitor business metrics - **Gate:** Positive A/B test OR iteration --- ## Approvals | Role | Name | Date | Signature | |------|------|------|-----------| | ML Lead | | | | | Product Owner | | | | | Marketing Lead | | | | | Engineering Manager | | | |46:["$","$L71",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792304068667,"state":{"data":{"json":{"archivedAt":null,"id":"seed_b2b87dff658cc600bb5313282ab109da","slug":"machine-learning","title":"Machine Learning","description":"Learn the mathematical and engineering foundations of machine learning, explore executable visualizers, implement algorithms, and validate dataset releases in repository exercises.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Supervised Learning","Unsupervised Learning","Neural Networks","Deep Learning","Reinforcement Learning","ML Engineering"],"estimatedHours":252.3,"problemCount":39,"visualizerCount":12,"quizCount":0,"learningCount":277,"difficulty":"ADVANCED","idealFor":["Data scientists and ML engineers","Software engineers transitioning to ML","Researchers and academics","Anyone wanting to understand AI/ML deeply"],"prerequisites":["Basic programming (Python preferred)","High school math (algebra, basic calculus)","Comfort with mathematical notation"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithms","Deep learning architectures","Model training and evaluation","Feature engineering","ML system design"],"outcomes":["Build and train ML models from scratch","Understand the math behind algorithms","Apply appropriate algorithms to problems","Design neural network architectures","Deploy and maintain ML systems","Stay current with ML research"],"overview":"This comprehensive learning path takes you from mathematical foundations to cutting-edge ML techniques, preparing you for ML engineering and research roles.\n\n**What you'll learn:**\n• Mathematical foundations (linear algebra, calculus, probability)\n• Statistical learning theory\n• Regression and classification algorithms\n• Ensemble methods (Random Forests, XGBoost)\n• Model evaluation and hyperparameter tuning\n• Unsupervised learning (clustering, dimensionality reduction)\n• Bayesian methods and probabilistic models\n• Neural networks and deep learning\n• CNNs, RNNs, and Transformers\n• Generative models and reinforcement learning\n• ML engineering and deployment\n\nEach topic includes detailed explanations with mathematical rigor.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.124Z","modules":[{"id":"seed_458ccce80e8010395b21b166cb7157a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"What Is Machine Learning?","order":1,"contentType":"LEARNING","contentSlug":"what-is-machine-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_713656282288e703aa91193ff6640a1e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"The Machine Learning Pipeline","order":2,"contentType":"LEARNING","contentSlug":"the-ml-pipeline","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_265702d858afc6a50a187b96538c09bf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Key Concepts and Terminology","order":3,"contentType":"LEARNING","contentSlug":"key-concepts-and-terminology","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3f6d09c97b1e031f237b2abc340e3cbd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Machine Learning Success Factors","order":4,"contentType":"LEARNING","contentSlug":"ml-success-factors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_f1d78b0dba00cdd6d7891c54667b63c0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"What Is Machine Learning: The Foundation of Intelligent Systems","order":1,"contentType":"LEARNING","contentSlug":"what-is-ml","description":null,"estimatedMins":20,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3cd2b6f1422ffede96de1e0597239aa9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"The Machine Learning Landscape","order":2,"contentType":"LEARNING","contentSlug":"ml-landscape","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_be70ea380f07d6ea7e2bdcfc3143d40a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"When to Use Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"when-to-use-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_0160e8621c11b759ae3cc62ac74840f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_62c3a9c331653ca339db3e3f28ff8baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Eigenvalues and Eigenvectors","order":2,"contentType":"LEARNING","contentSlug":"eigenvalues-and-eigenvectors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_79a3d1325d227a51f27b9ef5234cd9a9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Matrix Decompositions","order":3,"contentType":"LEARNING","contentSlug":"matrix-decompositions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1a7e06e15bba9acf14df3d33c9f77125","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Projections and Least Squares","order":4,"contentType":"LEARNING","contentSlug":"projections-and-least-squares","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fe00df3a3ce80f8b8080b9d940134165","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":5,"contentType":"VISUALIZER","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_95423ea3af30d0a2a29f5e934888f366","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":6,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_58253712c8015db94782d197ddc36627","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Pairwise Matrix Product Computation","order":7,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_22c7972151c0c5ecdeb281eb35ce19d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":8,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_83ea72a6e731d4dbcd355861ca589959","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Matrices and Linear Transformations","order":1,"contentType":"LEARNING","contentSlug":"matrices-and-linear-transformations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_e52e478149e70c47f5ecdb61ff144516","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Norms and Distance Metrics","order":2,"contentType":"LEARNING","contentSlug":"norms-and-distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1b167b3e9032b1f5cd4490b4f4ef40b2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":3,"contentType":"VISUALIZER","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_e4f70753ddb0e7daa22b01cddae4d0cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":4,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d267450d674287a1391f487fe97633fa","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Feature Covariance Computation","order":5,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_715f767142a8ca5f68ecbe95188b9171","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_136b74dda11c6ede585436811675d040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Multivariate Calculus","order":2,"contentType":"LEARNING","contentSlug":"multivariate-calculus","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_5d93b2deb9a5171452b8f10b0927266b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Convex Optimization","order":3,"contentType":"LEARNING","contentSlug":"convex-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c17e63b9a5e2e3517a5c1a05e1c8687e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Descent","order":4,"contentType":"LEARNING","contentSlug":"gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_58a6b4d26b778b4a73205b09fd21b0f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Constrained Optimization","order":5,"contentType":"LEARNING","contentSlug":"constrained-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_b0bf23bffa2221135e1bfa0877fc16a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Advanced Optimization Methods","order":6,"contentType":"LEARNING","contentSlug":"advanced-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_87fed16558b28ac02f40835f8205b2eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":7,"contentType":"VISUALIZER","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1e177e352ea702e7bdeb96bc180c96b0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":8,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6538cb1ab6830bbd13c96c2518a5bbab","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Vector Computation for Scalar Fields","order":9,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_73aa88219e07d9fd5f5fb52f60c5bde7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_501a2bc6ca2690e1bb6027ebab947dbb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Random Variables and Distributions","order":2,"contentType":"LEARNING","contentSlug":"random-variables-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_4bedc7e467feee3964d3f6d9adc5c5a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Common Probability Distributions","order":3,"contentType":"LEARNING","contentSlug":"common-probability-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_de316035d6a3b7c5c82a9b8d273802de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Joint and Marginal Distributions","order":4,"contentType":"LEARNING","contentSlug":"joint-marginal-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3e205a6ba092f97e52401620397afc15","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Estimation Theory","order":5,"contentType":"LEARNING","contentSlug":"estimation-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_9d79f7e518e418caedc3c16ad4567312","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Information Theory for Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"information-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c5e69fdba5ec3502e7ecb9a2729a06be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Population Mean Confidence Estimator","order":7,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_5b652c4511070dff688b90df174e04cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Independent Samples Mean Comparison","order":8,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_66416329d1070e25819caacd6051f101","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Learning Problem","order":1,"contentType":"LEARNING","contentSlug":"the-learning-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_27b627da90e780a7304764b0313cb5a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"PAC Learning: Probably Approximately Correct Framework","order":2,"contentType":"LEARNING","contentSlug":"pac-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_91308bda8e0cece0354a33420f6bab7c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"VC Dimension and Model Complexity","order":3,"contentType":"LEARNING","contentSlug":"vc-dimension","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_74a06a67e984c7f33ea72321e27fe638","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Bias-Variance Tradeoff","order":4,"contentType":"LEARNING","contentSlug":"bias-variance-tradeoff","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6f12c2ff794f5c0a3d28930627f9e9ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Regularization Theory","order":5,"contentType":"LEARNING","contentSlug":"regularization-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_8848fbde5a5ec45b7dcbbb6bcd4ab9ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Generalization Bounds: Mathematical Guarantees for Learning","order":6,"contentType":"LEARNING","contentSlug":"generalization-bounds","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_82fa8bc43938f7a884129594f86d7d81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Regression Diagnostics","order":1,"contentType":"LEARNING","contentSlug":"regression-diagnostics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c6e739d9eb19caa6b379150681b5e7cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Computational Considerations in Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"computational-considerations-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_829f2ade22abec4e2b345562ce75988e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_f2efa2b99627070158e4ef4b79c540a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Multiple Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"multiple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a7bdfcdeda9f1afd7fb7b9fc202d3f81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Statistical Properties of OLS","order":3,"contentType":"LEARNING","contentSlug":"statistical-properties-of-ols","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_916ec3501e133742dec47a2102539082","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Maximum Likelihood View of Linear Regression","order":4,"contentType":"LEARNING","contentSlug":"maximum-likelihood-view","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_37c09e6af50bedd36be1dc9600378f95","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":5,"contentType":"VISUALIZER","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1eaf702ff974ccd2ce05bd4f9821f236","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":6,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_ff8d00adbbea598d4881affb39e91b05","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":7,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d97a7c01ffd0b34a093375e7578d029d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"The Overfitting Problem","order":1,"contentType":"LEARNING","contentSlug":"the-overfitting-problem","description":null,"estimatedMins":40,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_12992e935695a3adf10d331bc32c3762","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Ridge Regression (L2 Regularization)","order":2,"contentType":"LEARNING","contentSlug":"ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6bb2f7c3adb3a36845ac25fd5e6983ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Lasso Regression (L1 Regularization)","order":3,"contentType":"LEARNING","contentSlug":"lasso-regression-l1","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_467edb60b91d6f7d7c4cb891f946f6ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Elastic Net Regularization","order":4,"contentType":"LEARNING","contentSlug":"elastic-net","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_01a0334a7189eee091e1369be1d858ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Bayesian Interpretation of Regularization","order":5,"contentType":"LEARNING","contentSlug":"bayesian-interpretation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_539e0c0df2b9d3fc72bfe1ebe1830538","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Advanced Regularization Techniques","order":6,"contentType":"LEARNING","contentSlug":"advanced-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_598afbdc7bc7fa0451b024fbbb08b0a1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Kernel Ridge Regression","order":1,"contentType":"LEARNING","contentSlug":"kernel-ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_723eff8f5cc227d1626cccbd013817f2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Gaussian Processes Introduction","order":2,"contentType":"LEARNING","contentSlug":"gaussian-processes-introduction","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_cab2603b0eff0b9065ccf19343b8f194","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"The Kernel Trick","order":1,"contentType":"LEARNING","contentSlug":"the-kernel-trick","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_bc8bcb824c6863c43427f9eee6e7f874","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Gaussian Process Kernels and Hyperparameter Optimization","order":2,"contentType":"LEARNING","contentSlug":"gp-kernels-and-hyperparameters","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a8ccd12986493122718a2e8f610b8745","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Computational Aspects of Kernels","order":3,"contentType":"LEARNING","contentSlug":"computational-aspects-of-kernels","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6242715024a6f8cd75cba825c4cf2e87","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel Selection and Design","order":4,"contentType":"LEARNING","contentSlug":"kernel-selection-and-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_766bbb1d8379c0966fce86e377d8aa45","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel-Based Support Vector Classifier Training","order":5,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_480cc6f8d716ebf111dd33d18fc8ce41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Polynomial Regression","order":1,"contentType":"LEARNING","contentSlug":"polynomial-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6f3094d5cfb0e003129f88a934111408","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Spline Regression Methods","order":2,"contentType":"LEARNING","contentSlug":"spline-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fd56c51bbb955a03cd319577e559ca24","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Generalized Additive Models (GAMs)","order":3,"contentType":"LEARNING","contentSlug":"generalized-additive-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_23ca6d0844f4fd9cf359b5e42366acae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Quantile Regression","order":4,"contentType":"LEARNING","contentSlug":"quantile-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_20d9052919ee9f6e5926ffc684095840","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Robust Regression Methods","order":5,"contentType":"LEARNING","contentSlug":"robust-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d7ab2a7eb1f69de228f455ae070e7934","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Nonparametric Regression","order":6,"contentType":"LEARNING","contentSlug":"nonparametric-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_78a01a27b8d25b02cf4b06660cd62e0f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression And Glms","sectionOrder":14,"title":"Regularized Logistic Regression","order":1,"contentType":"LEARNING","contentSlug":"module-5-regularized-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fd8976631490d38a493a49d4ea06d921","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Binary Classification Problem","order":1,"contentType":"LEARNING","contentSlug":"binary-classification-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a84bde3ef9a84b2740b90dbf47496bc4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"The Logistic Regression Model","order":2,"contentType":"LEARNING","contentSlug":"logistic-regression-model","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_7c3373282cd644a5e90d3ce19e000cae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Maximum Likelihood Estimation for Logistic Regression","order":3,"contentType":"LEARNING","contentSlug":"mle-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_177b12207ca60d43846c02b30bdfc4ae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Multi-class Logistic Regression","order":4,"contentType":"LEARNING","contentSlug":"multi-class-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_700a56229a39f2c5efb792c516b003de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Generalized Linear Models (GLMs)","order":5,"contentType":"LEARNING","contentSlug":"generalized-linear-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5b5ecd2ea6321d0e366b6369851d7e1f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":6,"contentType":"VISUALIZER","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cc24bec346a9c06530806b63a487f8db","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":7,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94cb522600865b629be81feefa09685d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":8,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dceafab2ca6be5ae2808a6312c2333dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Decision Tree Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"decision-tree-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_66f262a333c03eff5f0ef18781c9d3af","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Splitting Criteria","order":2,"contentType":"LEARNING","contentSlug":"splitting-criteria","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_75b193ee8207ca9f7b2d2866489de306","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Growing Algorithms","order":3,"contentType":"LEARNING","contentSlug":"tree-growing-algorithms","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2460c7b303e40cec69e89a72b8b831e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Pruning and Regularization","order":4,"contentType":"LEARNING","contentSlug":"pruning-and-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f4d666cbe20d00df3882c0b2203eb2c8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Regression Trees","order":5,"contentType":"LEARNING","contentSlug":"regression-trees","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_60576ab2af9ebaac4b42cd4cfc97570c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Limitations and Extensions","order":6,"contentType":"LEARNING","contentSlug":"tree-limitations-and-extensions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5ecf05e48f7a0b21063fc124157e3b41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Recursive Classification Tree Builder","order":7,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6694bec14cdf4dec18589122c2ef6ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Maximum Margin Classifier","order":1,"contentType":"LEARNING","contentSlug":"maximum-margin-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_718289c9fa43b9c118e2c33a9b999fc7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Hard Margin SVM","order":2,"contentType":"LEARNING","contentSlug":"hard-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a42c0383f3c3f0b2cfe805c1906ef67e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Soft Margin SVM","order":3,"contentType":"LEARNING","contentSlug":"soft-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0817db7f4e635c79befbd2ec867d4208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Kernel Support Vector Machines","order":4,"contentType":"LEARNING","contentSlug":"kernel-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7f7f1f41aaf42cfff875745539c688c2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"SVM Optimization","order":5,"contentType":"LEARNING","contentSlug":"svm-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9d6a6fae9d4733d75d9d2c0ef2bf6689","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Multi-class Support Vector Machines","order":6,"contentType":"LEARNING","contentSlug":"multi-class-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2db3a376585980bc271d7b13445f187d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Gaussian Naive Bayes","order":1,"contentType":"LEARNING","contentSlug":"gaussian-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ccb1182e88952a8e41eacf6988a8f962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Linear and Quadratic Discriminant Analysis","order":2,"contentType":"LEARNING","contentSlug":"linear-and-quadratic-discriminant-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fbe1f6cf1357ee3876023e84eeebe2e1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Generative vs Discriminative Models","order":1,"contentType":"LEARNING","contentSlug":"generative-vs-discriminative","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1cc60ba5baf88bab74e808a1eb6e245f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Bayes Classifier","order":2,"contentType":"LEARNING","contentSlug":"bayes-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b88cd36a22d2096598e1f8434cad7cbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Naive Bayes Assumption","order":3,"contentType":"LEARNING","contentSlug":"naive-bayes-assumption","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c827e845d309186b4d828114f01bf6ea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Multinomial and Bernoulli Naive Bayes","order":4,"contentType":"LEARNING","contentSlug":"multinomial-bernoulli-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8e0dbdfdcaf7222bdf2b6330b80049eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Algorithm","order":1,"contentType":"LEARNING","contentSlug":"knn-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9a886703f21dde8f994accd7696e3962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Distance Metrics for K-Nearest Neighbors","order":2,"contentType":"LEARNING","contentSlug":"distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_beabe40abd8e31e1a3461563e488e5ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Curse of Dimensionality","order":3,"contentType":"LEARNING","contentSlug":"curse-of-dimensionality","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a3e281d02cea4be01efa2e8de9d21650","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Efficient K-Nearest Neighbors","order":4,"contentType":"LEARNING","contentSlug":"efficient-knn","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_48ce033aa3506c796b6262f9edcf55a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Voronoi Diagrams & Decision Surfaces","order":5,"contentType":"LEARNING","contentSlug":"voronoi-diagrams-and-decision-surfaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4186620863285c1d7b3ce960bbd55ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Locality-Sensitive Hashing (LSH)","order":6,"contentType":"LEARNING","contentSlug":"locality-sensitive-hashing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_02e815b47429e576631e0381220c4966","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Weighted KNN","order":7,"contentType":"LEARNING","contentSlug":"weighted-knn","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_63ff05a505bafec47763bc44bff3a983","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Variants","order":8,"contentType":"LEARNING","contentSlug":"knn-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bafb1e8a6af32317a42e273f5a15de1c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Ensemble Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"ensemble-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_22c2fdccde0ab9e52f64339f364e757d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Random Forests","order":2,"contentType":"LEARNING","contentSlug":"random-forests","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_00a4486bae311423e333adfc96315696","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Bagging for Other Models","order":3,"contentType":"LEARNING","contentSlug":"bagging-for-other-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c51429550b08bde8708ef8c1c162ef1d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Bootstrap Aggregating: Variance Reduction Through Ensemble Averaging","order":1,"contentType":"LEARNING","contentSlug":"bootstrap-aggregating","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_51f41e3368033fc0b10899ff25d5c5ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Feature Importance","order":2,"contentType":"LEARNING","contentSlug":"feature-importance","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5ad2b982504501af216bbc78e377ae2a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Random Forest Variants","order":3,"contentType":"LEARNING","contentSlug":"random-forest-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f03dc46090058acd74b559494ee32f72","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting Intuition","order":1,"contentType":"LEARNING","contentSlug":"boosting-intuition","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_013f75c45c26c5238649f80602c7eea3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"AdaBoost: Adaptive Boosting","order":2,"contentType":"LEARNING","contentSlug":"adaboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_747bf0ebc82590cbd3552b19fb0b76e9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting as Gradient Descent","order":3,"contentType":"LEARNING","contentSlug":"boosting-as-gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_51f106abb54ddee63c6fb37b54ff5b4a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Gradient Boosting","order":4,"contentType":"LEARNING","contentSlug":"gradient-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dc5adc297f1955b0a18ca701a518df00","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Loss Functions for Boosting","order":5,"contentType":"LEARNING","contentSlug":"loss-functions-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8af9b78e83ce53d210e580fa87ed569b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Regularization in Boosting","order":6,"contentType":"LEARNING","contentSlug":"regularization-in-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fb17f19632cfed0944218bf0287a019f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"XGBoost: Extreme Gradient Boosting","order":1,"contentType":"LEARNING","contentSlug":"xgboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f4ed4ac2f15096908a3839a371aea45d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"LightGBM: Light Gradient Boosting Machine","order":2,"contentType":"LEARNING","contentSlug":"lightgbm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2c9e0dfc3b67f3a1c9330f30e269636b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"CatBoost","order":3,"contentType":"LEARNING","contentSlug":"catboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8a969e8156debd6e794f66d4ba1b8f47","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Hyperparameter Tuning for Boosting","order":4,"contentType":"LEARNING","contentSlug":"hyperparameter-tuning-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b3cc7ecb9af183e57d91f1d32acd0ef2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Feature Engineering for Boosting","order":5,"contentType":"LEARNING","contentSlug":"feature-engineering-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0f0a3fcb7bd62041bb7bef139cbd5bdc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Boosting Best Practices","order":6,"contentType":"LEARNING","contentSlug":"boosting-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ac4e81c68db8fe1d9ac89997df18a501","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c5ef7edae79ba6a58e3fd0d44017ac94","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"ROC and Precision-Recall Curves","order":2,"contentType":"LEARNING","contentSlug":"roc-and-precision-recall-curves","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7e80b225bee84e82f8ac145be8ea9c03","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Multi-class Classification Metrics","order":3,"contentType":"LEARNING","contentSlug":"multi-class-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4d46bb09037b0706babb1caba43f8b9d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Regression Metrics","order":4,"contentType":"LEARNING","contentSlug":"regression-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_43ad46d64dbce2d1516e1fb067ceaf9a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Ranking Metrics","order":5,"contentType":"LEARNING","contentSlug":"ranking-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_817f0073a4cafb13f8f380c749e23c80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Custom and Business Metrics","order":6,"contentType":"LEARNING","contentSlug":"custom-and-business-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d59e5e1e4d128d743b25c62154ab3fbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Softmax Probability Distribution","order":7,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_347487ee43f17af456b511a19a40dca7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Holdout Validation","order":1,"contentType":"LEARNING","contentSlug":"holdout-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e0201302cbabe1c8ac59d7eb9fd7b3b5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Stratified and Group Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"stratified-and-group-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5817575eae860da4b31aff4fbbe86bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Bootstrap Methods","order":3,"contentType":"LEARNING","contentSlug":"bootstrap-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0a5077e15e4a5548c820847a82b9c107","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"K-Fold Cross-Validation","order":1,"contentType":"LEARNING","contentSlug":"k-fold-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_80bb08d6076b67b40673dbd30ce30996","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Time Series Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"time-series-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4caea31c12e70b94b3449288ee507316","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Nested Cross-Validation","order":3,"contentType":"LEARNING","contentSlug":"nested-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c39b44e15b86f86fb46fad967dc035f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Data Partitioning with Cross-Validation Splits","order":4,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a7879f98db25ce0269f64ef3f5fe4bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Hyperparameter Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"hyperparameter-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94e0b48f0fb220a06f9766ad9d913f80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Grid Search","order":2,"contentType":"LEARNING","contentSlug":"grid-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b3ebbda137c88b6799101f8804197d6d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Random Search","order":3,"contentType":"LEARNING","contentSlug":"random-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_eb2d1d9b92ee8ca33315169f186c57a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Bayesian Optimization","order":4,"contentType":"LEARNING","contentSlug":"bayesian-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_217594c65573968f0c532e09b11d0155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Multi-Fidelity Optimization","order":5,"contentType":"LEARNING","contentSlug":"multi-fidelity-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cd56aef607f08e69d72c3e7d013c7a26","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Advanced HPO Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-hpo-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dfc3625133e375f2ddb7281b904bc083","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Problem Formulation","order":1,"contentType":"LEARNING","contentSlug":"clustering-problem-formulation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_677d6ef0d11ced3abb40fe90153268f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Clustering","order":2,"contentType":"LEARNING","contentSlug":"k-means-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d3f71756678ae1cb6211450c7d2ca5ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Variants: Beyond Standard K-Means Clustering","order":3,"contentType":"LEARNING","contentSlug":"k-means-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ad45ea7d5ec46859780dfb1e1b634e18","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Hierarchical Clustering","order":4,"contentType":"LEARNING","contentSlug":"hierarchical-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_781388f8e96bee94ce065ff1db0c0064","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Spectral Clustering","order":5,"contentType":"LEARNING","contentSlug":"spectral-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d758ff45163e4d63f66bd744695a183b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Evaluation","order":6,"contentType":"LEARNING","contentSlug":"clustering-evaluation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_14546b73b6439e5fd290e7eac815870c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":7,"contentType":"VISUALIZER","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_11791ef708ec1c800feac411b9252ab8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_68bdb028486489eb91f411607b9412b6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Density Estimation Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"density-estimation-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_64066580a6eb406e58043cabb534f9da","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Kernel Density Estimation Theory","order":2,"contentType":"LEARNING","contentSlug":"kde-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4234b761eb1944905dac7fc3a64bf7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Gaussian Mixture Models","order":3,"contentType":"LEARNING","contentSlug":"gaussian-mixture-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a2eacc9551297ba9d8a48167d5c81781","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"The Expectation-Maximization Algorithm","order":1,"contentType":"LEARNING","contentSlug":"expectation-maximization-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_22d9cdb6f03f9f0e12d8f69cb35600cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Beyond Gaussian Mixtures","order":2,"contentType":"LEARNING","contentSlug":"beyond-gaussian-mixtures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8758cd49b4805ac155a60d016a0a2e38","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Density-Based Clustering","order":3,"contentType":"LEARNING","contentSlug":"density-based-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_983a6d13254cf67c792c3d012aa231b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Dimensionality Reduction Motivation","order":1,"contentType":"LEARNING","contentSlug":"dimensionality-reduction-motivation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_af0b27842aa6a92042251980b9650ad8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Analysis Theory","order":2,"contentType":"LEARNING","contentSlug":"pca-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0ae7eac699c5b7e0ea73d5f865fb72b9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"PCA Variants","order":3,"contentType":"LEARNING","contentSlug":"pca-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d09ca8e44e15e4da659e7b542cc04446","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Kernel PCA","order":4,"contentType":"LEARNING","contentSlug":"kernel-pca","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_85afe3c0f35cb0210def66038cb352a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Factor Analysis","order":5,"contentType":"LEARNING","contentSlug":"factor-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ab73773fa16c6ed214ba79504d07c444","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Independent Component Analysis","order":6,"contentType":"LEARNING","contentSlug":"independent-component-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c61ed49082d6e5a2589810425c9b31f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":7,"contentType":"VISUALIZER","contentSlug":"principal-component-extraction","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_01b5934d7f8ef0bf58ff57e63421b50f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":8,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1c389cf030159b7da28f11969faaa3b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Manifold Hypothesis","order":1,"contentType":"LEARNING","contentSlug":"manifold-hypothesis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c1c7cd66ec0fed623f6f8f579b7062f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Multidimensional Scaling","order":2,"contentType":"LEARNING","contentSlug":"multidimensional-scaling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_71ecb495764198e1daba7a26711139fc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Isomap: Isometric Feature Mapping","order":3,"contentType":"LEARNING","contentSlug":"isomap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_73855f5207a19a961f18fcd49f63f1a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Locally Linear Embedding","order":4,"contentType":"LEARNING","contentSlug":"locally-linear-embedding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_492a583a08bf6d335d7e2a481be94363","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"t-Distributed Stochastic Neighbor Embedding (t-SNE)","order":5,"contentType":"LEARNING","contentSlug":"t-sne","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_605375d1d6334b96f0eead774cc314f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"UMAP (Uniform Manifold Approximation and Projection)","order":6,"contentType":"LEARNING","contentSlug":"umap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bff5e7a0bc4fddf9a78ccd4982e6832d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Anomaly Detection Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"anomaly-detection-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a2dd0d8bf5ebd6be590cd9d205ca3b5a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Distance-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"distance-based-methods","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1a47f9a533ee25611d5c267af621d678","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Isolation Forest for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"isolation-forest","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76b018ba7d1f8fc744e0978c016eb04e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Statistical Methods for Anomaly Detection","order":1,"contentType":"LEARNING","contentSlug":"statistical-methods-anomaly-detection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a8fc19bd78ba2746be4fd62a27ece203","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Density-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"density-based-methods-anomaly","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4555f92571404920aa7a0ff10edd4942","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"One-Class Methods for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"one-class-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ef80465133d1fe9ce84bf4ab98e19dea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"The Bayesian Framework","order":1,"contentType":"LEARNING","contentSlug":"bayesian-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e20205c549adb1a21e00c02f2724d38b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Conjugate Priors","order":2,"contentType":"LEARNING","contentSlug":"conjugate-priors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b347cdec69ff8713db8903d02f3d40cd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Linear Regression","order":3,"contentType":"LEARNING","contentSlug":"bayesian-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_017d50d5c031b5e4d0e1445faf6ce982","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Model Comparison","order":4,"contentType":"LEARNING","contentSlug":"bayesian-model-comparison","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d310f58ff165b49c4ec608aa0227cacf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Sampling Methods","order":5,"contentType":"LEARNING","contentSlug":"approximate-inference-sampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5c847f1e908149fa7ac07f86188b319b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Deterministic Methods","order":6,"contentType":"LEARNING","contentSlug":"approximate-inference-deterministic","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_46d5fd4b152d1cd30b9443131d4b0c73","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Posterior Parameter Estimation with Conjugate Prior","order":7,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ca2a8840c6015e1ebbb86e35dbba1513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Probabilistic Graphical Models","order":1,"contentType":"LEARNING","contentSlug":"probabilistic-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f736480433bf6703491b1838f1c22be3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Bayesian Networks","order":2,"contentType":"LEARNING","contentSlug":"bayesian-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_29f3e6fc90cbda45c1970a76aa59d663","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Markov Random Fields","order":3,"contentType":"LEARNING","contentSlug":"markov-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_24b1625575d59d42572164bfa2537fd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Inference in Graphical Models","order":4,"contentType":"LEARNING","contentSlug":"inference-in-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_26906c87473209c81f340c709f82c90c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Hidden Markov Models","order":5,"contentType":"LEARNING","contentSlug":"hidden-markov-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_70298c6e59e6d4aa717b493f4727004f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Conditional Random Fields","order":6,"contentType":"LEARNING","contentSlug":"conditional-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bfcd86cd712852ba44877065af04967f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"gp-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_379822a695955a78bd4ea16f38131ea2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Regression","order":2,"contentType":"LEARNING","contentSlug":"gp-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9c7afac43216ccc7ff3ab116639b4514","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Kernel Design for Gaussian Processes","order":3,"contentType":"LEARNING","contentSlug":"kernel-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f84c8f27bdd419c923e2b1700bde6637","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Hyperparameter Learning in Gaussian Processes","order":4,"contentType":"LEARNING","contentSlug":"gp-hyperparameter-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6b381cf713c81aa18f4233121bab820e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Scalable Gaussian Processes","order":5,"contentType":"LEARNING","contentSlug":"scalable-gps","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f98eb34b8b26e060c9c2ee26e2db04e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Classification","order":6,"contentType":"LEARNING","contentSlug":"gp-classification","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_848b6363ea4fbdd529dd3bd0015d5968","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Inference Framework","order":1,"contentType":"LEARNING","contentSlug":"variational-inference-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_347f0f34f42595c07ce415157b687b80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Mean-Field Approximation","order":2,"contentType":"LEARNING","contentSlug":"mean-field-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e21603eecdb3d61c60b1ccc6bbea7857","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"ELBO Optimization","order":3,"contentType":"LEARNING","contentSlug":"elbo-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_546b35e4882116c3d0a51680f2d835be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Stochastic Variational Inference","order":4,"contentType":"LEARNING","contentSlug":"stochastic-variational-inference","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1463afd50f358c4c3ce2e904178881a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Autoencoders","order":5,"contentType":"LEARNING","contentSlug":"ml-variational-inference-variational-autoencoders","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e9b79bdc20ae45b33f8b2f486ee6c680","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Advanced VI Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-vi-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f82ea691ae02c2164db9a4a902cfee61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_763ec03462b28e436c290ff5a6028cdd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Multi-Layer Perceptrons","order":2,"contentType":"LEARNING","contentSlug":"multi-layer-perceptrons","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a798e45c47f2ce91b6ef5d8994a2778d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Universal Approximation","order":3,"contentType":"LEARNING","contentSlug":"universal-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_06736591bc987a4d2945f9be6f8633ce","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Functions","order":4,"contentType":"LEARNING","contentSlug":"activation-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ef1c8026b6efc42f60f2b27ef58f6ed5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Loss Functions","order":5,"contentType":"LEARNING","contentSlug":"loss-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6cc64eb0e0ba1c8951cbe4fed8d38c06","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Output Layers","order":6,"contentType":"LEARNING","contentSlug":"output-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dd77b6b05260cf5c413474e5d189ece4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":7,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0d1e9a5fd7d14cd9c1f49cb2ce5b143b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":8,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_81b4675ff4c64769f9e3ca3a0e12a3dc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Backpropagation Algorithm","order":1,"contentType":"LEARNING","contentSlug":"backpropagation-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6f6f054ba88424be3541f892015658c4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Gradient Descent Variants","order":2,"contentType":"LEARNING","contentSlug":"gradient-descent-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0d04bb8be0289593e83fa5d0b2ad1b85","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Adaptive Optimizers","order":3,"contentType":"LEARNING","contentSlug":"adaptive-optimizers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_790d19157f66aa34b92db31dd4a5173b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Learning Rate Schedules: Orchestrating Optimization Dynamics","order":4,"contentType":"LEARNING","contentSlug":"learning-rate-schedules","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_62fc9f7677c99703f946f7592b4dd208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Computational Graphs","order":1,"contentType":"LEARNING","contentSlug":"computational-graphs","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b49f7466079a1b6662ac948c9e560dae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Second-Order Optimization Methods","order":2,"contentType":"LEARNING","contentSlug":"second-order-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c856bc9ccfc814ca27e37c25317189eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":3,"contentType":"VISUALIZER","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5e38cad8f738a9edc45cd7bb25048d92","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":4,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_79363c50d7cc2df59cf264620abf9d14","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Gradient Computation Engine (Basic Autograd)","order":5,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1a37ecaa3606e5645687fde6f75686e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Capacity and Generalization","order":1,"contentType":"LEARNING","contentSlug":"capacity-and-generalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4556ac961fca197a64db759fdfd37ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Weight Regularization","order":2,"contentType":"LEARNING","contentSlug":"weight-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_28b239ae9dfb30b8bddecfbaa3928c28","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Dropout","order":3,"contentType":"LEARNING","contentSlug":"dropout","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e44f2f1dee2954db26113a899c23bb10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Batch Normalization","order":4,"contentType":"LEARNING","contentSlug":"batch-normalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_09158c8adb4d1dbd43a885617bdbd7b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Data Augmentation: Expanding Training Distributions","order":5,"contentType":"LEARNING","contentSlug":"data-augmentation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_020e4e8a002e13bfe417d1aac7d02803","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Implicit Regularization in Deep Learning","order":6,"contentType":"LEARNING","contentSlug":"implicit-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6851d7814e8e4b64902dfba5a609d2de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94702d5823572341bdb7c6dac7a81e0d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Convolutional Layers","order":2,"contentType":"LEARNING","contentSlug":"convolutional-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4af0cc0135bdb4df0247addfc29fe2ee","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Pooling and Downsampling","order":3,"contentType":"LEARNING","contentSlug":"pooling-and-downsampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cd8e784eebe78b202a0a8c12e492d917","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"CNN Architectures: From LeNet to Modern Designs","order":4,"contentType":"LEARNING","contentSlug":"cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3437e70f0dc0731e390eaacb69b9232e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Residual Networks and Skip Connections","order":5,"contentType":"LEARNING","contentSlug":"residual-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_308ce9bd101b8dd160bbae4d0fb7454d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Modern CNN Architectures","order":6,"contentType":"LEARNING","contentSlug":"modern-cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cbee09a0e607620cd98f7ddd2e9fa3d1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":7,"contentType":"VISUALIZER","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_110330320fbff6e7329228ba2e790938","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":8,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5f1f573d9929ca2f3046a5cc8bcc8aca","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5db2b8211deeb14b772472b6b9cf59a0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Recurrent Neural Network Architecture","order":2,"contentType":"LEARNING","contentSlug":"rnn-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_162d876b2b2a4f4479fbf1d37a5e4a64","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Vanishing and Exploding Gradients","order":3,"contentType":"LEARNING","contentSlug":"vanishing-exploding-gradients","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3820671dcdd4232fc4cdd4d868cea6d2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Long Short-Term Memory","order":4,"contentType":"LEARNING","contentSlug":"long-short-term-memory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fa7c5556299f5f97c15a403a7b11cb8c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Gated Recurrent Units","order":5,"contentType":"LEARNING","contentSlug":"gated-recurrent-units","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_37f61dfc75c977d4f49c5d259f50879f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Advanced RNN Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-rnn-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_78f2403d64ba60eab0edef165e79785d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":7,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e56a4e18774f4156d0a77d0180048778","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":8,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6a5518e35a9be244d03d87d3d56f50e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e9ba71f8e6d0d3c6af728ccfb81965d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention","order":2,"contentType":"LEARNING","contentSlug":"self-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2e1937ca8c0da75933b343ae37dc2cd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Multi-Head Attention","order":3,"contentType":"LEARNING","contentSlug":"multi-head-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_042e91151586cd6a2bb93b767eee318d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Architecture","order":4,"contentType":"LEARNING","contentSlug":"transformer-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_79661700b7c47fae120d0bd30aa077f8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Variants","order":5,"contentType":"LEARNING","contentSlug":"transformer-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2c16c77d2922828776983c6932e17364","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":6,"contentType":"VISUALIZER","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6ea15e630262800d691de2dbdb9277ad","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":7,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ea4826b8cb5aa5c6c91df6bbd5b3562f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention Transformers","sectionOrder":47,"title":"Positional Encoding","order":1,"contentType":"LEARNING","contentSlug":"positional-encoding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8a58a3ebe0e519a060e593bb2f787e77","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_04aa20659bf19165d9aef4e2b23a6ff7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Adversarial Networks","order":2,"contentType":"LEARNING","contentSlug":"generative-adversarial-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fbcfefe6c406993e3296659d69308542","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"GAN Variants","order":3,"contentType":"LEARNING","contentSlug":"gan-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b7c820446f87f68f23f8ec05b9981baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Flow-Based Models","order":4,"contentType":"LEARNING","contentSlug":"flow-based-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ec6ae3647c56081215e3cbf9ded2e8dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Diffusion Models","order":5,"contentType":"LEARNING","contentSlug":"diffusion-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_904927660fd8d6c41d1aba5f40bf3672","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised And Self Supervised Learning","sectionOrder":49,"title":"The Label Scarcity Problem","order":1,"contentType":"LEARNING","contentSlug":"the-label-scarcity-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e4208222d89c41fea5968e9e5f3c53d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Semi-Supervised Methods","order":1,"contentType":"LEARNING","contentSlug":"semi-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3bff5f8dfe463704b7b4b20e9fa5d91c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Consistency Regularization","order":2,"contentType":"LEARNING","contentSlug":"consistency-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cef5c1d33e1344056ba5a145591e865b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Self-Supervised Learning","order":3,"contentType":"LEARNING","contentSlug":"self-supervised-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_28431ac2324f528d7232189a00afdae7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Contrastive Learning","order":4,"contentType":"LEARNING","contentSlug":"contrastive-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_89bfe981eb096f5677324a5cb95c5c3f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Modern Self-Supervised Methods","order":5,"contentType":"LEARNING","contentSlug":"modern-self-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_48b71cced9af84fce00dd5f87802a040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning And Domain Adaptation","sectionOrder":51,"title":"Multi-Task Learning","order":1,"contentType":"LEARNING","contentSlug":"multi-task-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bad0d98a933a15ea25cabed00cac2414","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Transfer Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"transfer-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_18d69040c7cd9a295f5552d746a8012b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Feature-Based Transfer","order":2,"contentType":"LEARNING","contentSlug":"feature-based-transfer","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8e9f01590c85745935d1f7e223be6927","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Fine-Tuning Strategies","order":3,"contentType":"LEARNING","contentSlug":"fine-tuning-strategies","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4c02fca3ba18a5932d556e4a7780eeeb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Domain Adaptation","order":4,"contentType":"LEARNING","contentSlug":"domain-adaptation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3d3815ce091181ea37c5c2491dc123c9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Meta-Learning: Learning to Learn","order":5,"contentType":"LEARNING","contentSlug":"meta-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_146bb4046d37075972f5f1d4a35d8133","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4a14d44ad08d752faaf01a1fe34edd4b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Markov Decision Processes","order":2,"contentType":"LEARNING","contentSlug":"markov-decision-processes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e962e18f072550c1bddf2875fc11d4d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Value-Based Methods","order":3,"contentType":"LEARNING","contentSlug":"value-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8dafdc1a94769c8c45e42c0a06ed363b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Policy-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"policy-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_306e6c5664e5b5241b8ca0658b77ed61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Deep Reinforcement Learning","order":5,"contentType":"LEARNING","contentSlug":"deep-reinforcement-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_83233c3c4eba3d8393e4b5df2a901c2f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Applications and Challenges","order":6,"contentType":"LEARNING","contentSlug":"rl-applications-and-challenges","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_538da3bc0ccbd37d3fab29f08eb07443","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Intra-Group Advantage Normalization for Policy Training","order":7,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f11572f4df3cd111b29c1115c8d584f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":8,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8ab2ad29bcfe76d03a61e3fdfc68781b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7b67e7eda540fa724d88ee493898b69c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Collaborative Filtering","order":2,"contentType":"LEARNING","contentSlug":"collaborative-filtering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8b451bfeded3345408e779b5bc2b7fb1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Matrix Factorization for Recommendations","order":3,"contentType":"LEARNING","contentSlug":"matrix-factorization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_19703d4984d6329646468830d58dec7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Content-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"content-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2ab9a2c09cd93ba87112ecc170e53513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Deep Learning for Recommendations","order":5,"contentType":"LEARNING","contentSlug":"deep-learning-for-recommendations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2af5ea0db1dad6b70b5104436968ca34","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Production Considerations","order":6,"contentType":"LEARNING","contentSlug":"production-considerations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_388dd9ab336383cfe8b60203b6a21a39","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5de18599b9e5181b808780cb9c304da9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Selection Theory","order":2,"contentType":"LEARNING","contentSlug":"feature-selection-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_497f3f5dbb61fb7014f109d9f0eb336b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Text Feature Engineering: N-grams","order":3,"contentType":"LEARNING","contentSlug":"text-feature-engineering-ngrams","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7f90aa61bc4fb373dafbad502a5f0fd6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":4,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_741941446f08f63cd865ccae73a1a71f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Categorical Binary Encoding","order":5,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7df14192fe8a798ae765e3b012cede59","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Ordered Polynomial Feature Generator","order":6,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_842f5163e5a439bbac1ef1918b42633a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_81d80261d64ffea6e4533de78d2b9dd0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Stores: The Foundation of Production ML Feature Management","order":2,"contentType":"LEARNING","contentSlug":"feature-stores","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f944d98b4893ec36ff4f28076308eeb2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Transformation Pipelines","order":3,"contentType":"LEARNING","contentSlug":"feature-transformation-pipelines","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cc3d8ae2e097a16d9fcc802ad211391b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"High-Cardinality Features","order":4,"contentType":"LEARNING","contentSlug":"high-cardinality-features","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a27695ebceaabefd2a3d7ab9d65fb829","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering - Basics","order":5,"contentType":"LEARNING","contentSlug":"text-feature-engineering-basics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1c77cb034b5085130e3be375725cf1a3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering: TF-IDF","order":6,"contentType":"LEARNING","contentSlug":"text-feature-engineering-tfidf","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7491c6ce5e01ba00b2b823909fe0d32a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Preprocessing: Transforming Raw Text for Machine Learning","order":7,"contentType":"LEARNING","contentSlug":"text-preprocessing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7980b68a9f4f240de52a46472d229990","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Word Embeddings","order":8,"contentType":"LEARNING","contentSlug":"word-embeddings","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a9d7121a5834d039ddce6eec079e2e25","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Overview","order":1,"contentType":"LEARNING","contentSlug":"automl-overview","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4822c3e211b027bbc0b9e39d013cd2bd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Preprocessing","order":2,"contentType":"LEARNING","contentSlug":"automated-preprocessing","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ae082beb489cc334aefa7f1daded438d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Model Selection","order":3,"contentType":"LEARNING","contentSlug":"automated-model-selection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e1b7d2e67067c90c33a877fa1c26d3e6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Neural Architecture Search","order":4,"contentType":"LEARNING","contentSlug":"neural-architecture-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_47b36d588fcad669b42eea822d760f0a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Systems","order":5,"contentType":"LEARNING","contentSlug":"automl-systems","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cb9a45dc1c54e73311b24cd928b3ab69","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Best Practices","order":6,"contentType":"LEARNING","contentSlug":"automl-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1458bdc7f12dad04db575fa4a57fe870","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability And Fairness","sectionOrder":58,"title":"Feature Attribution Methods","order":1,"contentType":"LEARNING","contentSlug":"feature-attribution-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0f88251e409f204b5dcec58e017412d4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Interpretability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"interpretability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_09729918b026512bb25f63a10b88afa7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Model-Specific Interpretability","order":2,"contentType":"LEARNING","contentSlug":"model-specific-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6377599b4a10d0e02c1cfc2dc7aaf743","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Fairness in Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"fairness-in-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4951de29801ee8a44a8759a1597460f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Bias Detection and Mitigation","order":4,"contentType":"LEARNING","contentSlug":"bias-detection-mitigation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2a221429a71db5df8da0275ce4697a10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Practical Interpretability","order":5,"contentType":"LEARNING","contentSlug":"practical-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4064079bf9971ca27d16886141b8871","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Foundation Models","order":1,"contentType":"LEARNING","contentSlug":"foundation-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_50bdd845bc425620c7bd09e44d4f5827","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Large Language Models","order":2,"contentType":"LEARNING","contentSlug":"large-language-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_db062e2abf514009df75d477ba2962e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Multimodal Learning: Bridging Vision, Language, and Beyond","order":3,"contentType":"LEARNING","contentSlug":"multimodal-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e885aaae2eb8f75705604e9d2293f2ec","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Federated Learning","order":4,"contentType":"LEARNING","contentSlug":"federated-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f6da44328600f666e7d9a74d1c32b432","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Continual Learning: Learning Without Forgetting","order":5,"contentType":"LEARNING","contentSlug":"continual-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3efeb1d46a6097c7b10ddb9525c4b581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Emerging Directions in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"emerging-directions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_723cca038a2c40f84476ae0e31ab18e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dbfa0d7a5ff41a228873f58339420ef9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Debugging ML Models","order":2,"contentType":"LEARNING","contentSlug":"debugging-ml-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_29daeedf46dc1d31c9070e587276c581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Project Management","order":3,"contentType":"LEARNING","contentSlug":"ml-project-management","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_800ab48cf1731556a258ea5ec18b78f6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Reading Research Papers","order":4,"contentType":"LEARNING","contentSlug":"reading-research-papers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f292b4c2034d13213b3e7c675b18f765","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Interviews: Complete Preparation Guide","order":5,"contentType":"LEARNING","contentSlug":"ml-interviews","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_632dc2189cf690119e3942c3dfa69d8b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Career Development in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"career-development","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_39cac80dede8b891307a5865480e576b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Dataset Batch Generator for Training Pipelines","order":7,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76bec3396a0c10a07b961c0659f31f5b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Performance Metrics Calculator","order":8,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d8bc35e4e84cf3a27ee560839206c155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Request Batching","order":9,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_07789f50da99325787e488c27c2f335e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Synchronized Data Permutation","order":1,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0818b627401c3528be0e63d2b248925b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"One bad dataset row consumes several quality allowances","order":2,"contentType":"REPO","contentSlug":"dataset-gate-failure-budget","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76aa74612887d6b05ec63a158ebb9b68","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Count bad dataset rows once","order":3,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-failure-budget","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_82746decf240c4d206937e214136ffe6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Published datasets differ from the snapshot that passed review","order":4,"contentType":"REPO","contentSlug":"dataset-gate-unchecked-publication","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6a6c35120fbb3874411dee3e7217348d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Publish the rows captured by the quality run","order":5,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-unchecked-publication","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"}],"sections":[{"title":"Chapter 1: Introduction To Machine Learning","order":1,"modules":["$46:props:state:queries:0:state:data:json:modules:0","$46:props:state:queries:0:state:data:json:modules:1","$46:props:state:queries:0:state:data:json:modules:2","$46:props:state:queries:0:state:data:json:modules:3"]},{"title":"Chapter 1: Introduction To Ml","order":2,"modules":["$46:props:state:queries:0:state:data:json:modules:4","$46:props:state:queries:0:state:data:json:modules:5","$46:props:state:queries:0:state:data:json:modules:6"]},{"title":"Chapter 2: Linear Algebra","order":3,"modules":["$46:props:state:queries:0:state:data:json:modules:7","$46:props:state:queries:0:state:data:json:modules:8","$46:props:state:queries:0:state:data:json:modules:9","$46:props:state:queries:0:state:data:json:modules:10","$46:props:state:queries:0:state:data:json:modules:11","$46:props:state:queries:0:state:data:json:modules:12","$46:props:state:queries:0:state:data:json:modules:13","$46:props:state:queries:0:state:data:json:modules:14"]},{"title":"Chapter 2: Mathematical Foundations Linear Algebra","order":4,"modules":["$46:props:state:queries:0:state:data:json:modules:15","$46:props:state:queries:0:state:data:json:modules:16","$46:props:state:queries:0:state:data:json:modules:17","$46:props:state:queries:0:state:data:json:modules:18","$46:props:state:queries:0:state:data:json:modules:19"]},{"title":"Chapter 3: Calculus Optimization","order":5,"modules":["$46:props:state:queries:0:state:data:json:modules:20","$46:props:state:queries:0:state:data:json:modules:21","$46:props:state:queries:0:state:data:json:modules:22","$46:props:state:queries:0:state:data:json:modules:23","$46:props:state:queries:0:state:data:json:modules:24","$46:props:state:queries:0:state:data:json:modules:25","$46:props:state:queries:0:state:data:json:modules:26","$46:props:state:queries:0:state:data:json:modules:27","$46:props:state:queries:0:state:data:json:modules:28"]},{"title":"Chapter 4: Probability Theory","order":6,"modules":["$46:props:state:queries:0:state:data:json:modules:29","$46:props:state:queries:0:state:data:json:modules:30","$46:props:state:queries:0:state:data:json:modules:31","$46:props:state:queries:0:state:data:json:modules:32","$46:props:state:queries:0:state:data:json:modules:33","$46:props:state:queries:0:state:data:json:modules:34","$46:props:state:queries:0:state:data:json:modules:35","$46:props:state:queries:0:state:data:json:modules:36"]},{"title":"Chapter 5: Statistical Learning Theory","order":7,"modules":["$46:props:state:queries:0:state:data:json:modules:37","$46:props:state:queries:0:state:data:json:modules:38","$46:props:state:queries:0:state:data:json:modules:39","$46:props:state:queries:0:state:data:json:modules:40","$46:props:state:queries:0:state:data:json:modules:41","$46:props:state:queries:0:state:data:json:modules:42"]},{"title":"Chapter 6: Linear Regression","order":8,"modules":["$46:props:state:queries:0:state:data:json:modules:43","$46:props:state:queries:0:state:data:json:modules:44"]},{"title":"Chapter 6: Linear Regression Deep Dive","order":9,"modules":["$46:props:state:queries:0:state:data:json:modules:45","$46:props:state:queries:0:state:data:json:modules:46","$46:props:state:queries:0:state:data:json:modules:47","$46:props:state:queries:0:state:data:json:modules:48","$46:props:state:queries:0:state:data:json:modules:49","$46:props:state:queries:0:state:data:json:modules:50","$46:props:state:queries:0:state:data:json:modules:51"]},{"title":"Chapter 7: Regularization Theory","order":10,"modules":["$46:props:state:queries:0:state:data:json:modules:52","$46:props:state:queries:0:state:data:json:modules:53","$46:props:state:queries:0:state:data:json:modules:54","$46:props:state:queries:0:state:data:json:modules:55","$46:props:state:queries:0:state:data:json:modules:56","$46:props:state:queries:0:state:data:json:modules:57"]},{"title":"Chapter 8: Kernel Methods","order":11,"modules":["$46:props:state:queries:0:state:data:json:modules:58","$46:props:state:queries:0:state:data:json:modules:59"]},{"title":"Chapter 8: Kernel Methods For Regression","order":12,"modules":["$46:props:state:queries:0:state:data:json:modules:60","$46:props:state:queries:0:state:data:json:modules:61","$46:props:state:queries:0:state:data:json:modules:62","$46:props:state:queries:0:state:data:json:modules:63","$46:props:state:queries:0:state:data:json:modules:64"]},{"title":"Chapter 9: Advanced Regression","order":13,"modules":["$46:props:state:queries:0:state:data:json:modules:65","$46:props:state:queries:0:state:data:json:modules:66","$46:props:state:queries:0:state:data:json:modules:67","$46:props:state:queries:0:state:data:json:modules:68","$46:props:state:queries:0:state:data:json:modules:69","$46:props:state:queries:0:state:data:json:modules:70"]},{"title":"Chapter 10: Logistic Regression And Glms","order":14,"modules":["$46:props:state:queries:0:state:data:json:modules:71"]},{"title":"Chapter 10: Logistic Regression Glms","order":15,"modules":["$46:props:state:queries:0:state:data:json:modules:72","$46:props:state:queries:0:state:data:json:modules:73","$46:props:state:queries:0:state:data:json:modules:74","$46:props:state:queries:0:state:data:json:modules:75","$46:props:state:queries:0:state:data:json:modules:76","$46:props:state:queries:0:state:data:json:modules:77","$46:props:state:queries:0:state:data:json:modules:78","$46:props:state:queries:0:state:data:json:modules:79"]},{"title":"Chapter 11: Decision Trees","order":16,"modules":["$46:props:state:queries:0:state:data:json:modules:80","$46:props:state:queries:0:state:data:json:modules:81","$46:props:state:queries:0:state:data:json:modules:82","$46:props:state:queries:0:state:data:json:modules:83","$46:props:state:queries:0:state:data:json:modules:84","$46:props:state:queries:0:state:data:json:modules:85","$46:props:state:queries:0:state:data:json:modules:86"]},{"title":"Chapter 12: Support Vector Machines","order":17,"modules":["$46:props:state:queries:0:state:data:json:modules:87","$46:props:state:queries:0:state:data:json:modules:88","$46:props:state:queries:0:state:data:json:modules:89","$46:props:state:queries:0:state:data:json:modules:90","$46:props:state:queries:0:state:data:json:modules:91","$46:props:state:queries:0:state:data:json:modules:92"]},{"title":"Chapter 13: Naive Bayes And Probabilistic Classifiers","order":18,"modules":["$46:props:state:queries:0:state:data:json:modules:93","$46:props:state:queries:0:state:data:json:modules:94"]},{"title":"Chapter 13: Naive Bayes Probabilistic Classifiers","order":19,"modules":["$46:props:state:queries:0:state:data:json:modules:95","$46:props:state:queries:0:state:data:json:modules:96","$46:props:state:queries:0:state:data:json:modules:97","$46:props:state:queries:0:state:data:json:modules:98"]},{"title":"Chapter 14: K Nearest Neighbors","order":20,"modules":["$46:props:state:queries:0:state:data:json:modules:99","$46:props:state:queries:0:state:data:json:modules:100","$46:props:state:queries:0:state:data:json:modules:101","$46:props:state:queries:0:state:data:json:modules:102","$46:props:state:queries:0:state:data:json:modules:103","$46:props:state:queries:0:state:data:json:modules:104","$46:props:state:queries:0:state:data:json:modules:105","$46:props:state:queries:0:state:data:json:modules:106"]},{"title":"Chapter 15: Bagging And Random Forests","order":21,"modules":["$46:props:state:queries:0:state:data:json:modules:107","$46:props:state:queries:0:state:data:json:modules:108","$46:props:state:queries:0:state:data:json:modules:109"]},{"title":"Chapter 15: Bagging Random Forests","order":22,"modules":["$46:props:state:queries:0:state:data:json:modules:110","$46:props:state:queries:0:state:data:json:modules:111","$46:props:state:queries:0:state:data:json:modules:112"]},{"title":"Chapter 16: Boosting Theory","order":23,"modules":["$46:props:state:queries:0:state:data:json:modules:113","$46:props:state:queries:0:state:data:json:modules:114","$46:props:state:queries:0:state:data:json:modules:115","$46:props:state:queries:0:state:data:json:modules:116","$46:props:state:queries:0:state:data:json:modules:117","$46:props:state:queries:0:state:data:json:modules:118"]},{"title":"Chapter 17: Modern Boosting","order":24,"modules":["$46:props:state:queries:0:state:data:json:modules:119","$46:props:state:queries:0:state:data:json:modules:120","$46:props:state:queries:0:state:data:json:modules:121","$46:props:state:queries:0:state:data:json:modules:122","$46:props:state:queries:0:state:data:json:modules:123","$46:props:state:queries:0:state:data:json:modules:124"]},{"title":"Chapter 18: Model Evaluation Metrics","order":25,"modules":["$46:props:state:queries:0:state:data:json:modules:125","$46:props:state:queries:0:state:data:json:modules:126","$46:props:state:queries:0:state:data:json:modules:127","$46:props:state:queries:0:state:data:json:modules:128","$46:props:state:queries:0:state:data:json:modules:129","$46:props:state:queries:0:state:data:json:modules:130","$46:props:state:queries:0:state:data:json:modules:131"]},{"title":"Chapter 19: Cross Validation And Resampling","order":26,"modules":["$46:props:state:queries:0:state:data:json:modules:132","$46:props:state:queries:0:state:data:json:modules:133","$46:props:state:queries:0:state:data:json:modules:134"]},{"title":"Chapter 19: Cross Validation Resampling","order":27,"modules":["$46:props:state:queries:0:state:data:json:modules:135","$46:props:state:queries:0:state:data:json:modules:136","$46:props:state:queries:0:state:data:json:modules:137","$46:props:state:queries:0:state:data:json:modules:138"]},{"title":"Chapter 20: Hyperparameter Optimization","order":28,"modules":["$46:props:state:queries:0:state:data:json:modules:139","$46:props:state:queries:0:state:data:json:modules:140","$46:props:state:queries:0:state:data:json:modules:141","$46:props:state:queries:0:state:data:json:modules:142","$46:props:state:queries:0:state:data:json:modules:143","$46:props:state:queries:0:state:data:json:modules:144"]},{"title":"Chapter 21: Clustering Algorithms","order":29,"modules":["$46:props:state:queries:0:state:data:json:modules:145","$46:props:state:queries:0:state:data:json:modules:146","$46:props:state:queries:0:state:data:json:modules:147","$46:props:state:queries:0:state:data:json:modules:148","$46:props:state:queries:0:state:data:json:modules:149","$46:props:state:queries:0:state:data:json:modules:150","$46:props:state:queries:0:state:data:json:modules:151","$46:props:state:queries:0:state:data:json:modules:152"]},{"title":"Chapter 22: Density Estimation And Mixture Models","order":30,"modules":["$46:props:state:queries:0:state:data:json:modules:153","$46:props:state:queries:0:state:data:json:modules:154","$46:props:state:queries:0:state:data:json:modules:155"]},{"title":"Chapter 22: Density Estimation Mixture Models","order":31,"modules":["$46:props:state:queries:0:state:data:json:modules:156","$46:props:state:queries:0:state:data:json:modules:157","$46:props:state:queries:0:state:data:json:modules:158"]},{"title":"Chapter 23: Dimensionality Reduction","order":32,"modules":["$46:props:state:queries:0:state:data:json:modules:159","$46:props:state:queries:0:state:data:json:modules:160","$46:props:state:queries:0:state:data:json:modules:161","$46:props:state:queries:0:state:data:json:modules:162","$46:props:state:queries:0:state:data:json:modules:163","$46:props:state:queries:0:state:data:json:modules:164","$46:props:state:queries:0:state:data:json:modules:165","$46:props:state:queries:0:state:data:json:modules:166"]},{"title":"Chapter 24: Manifold Learning","order":33,"modules":["$46:props:state:queries:0:state:data:json:modules:167","$46:props:state:queries:0:state:data:json:modules:168","$46:props:state:queries:0:state:data:json:modules:169","$46:props:state:queries:0:state:data:json:modules:170","$46:props:state:queries:0:state:data:json:modules:171","$46:props:state:queries:0:state:data:json:modules:172"]},{"title":"Chapter 25: Anomaly And Outlier Detection","order":34,"modules":["$46:props:state:queries:0:state:data:json:modules:173","$46:props:state:queries:0:state:data:json:modules:174","$46:props:state:queries:0:state:data:json:modules:175"]},{"title":"Chapter 25: Anomaly Outlier Detection","order":35,"modules":["$46:props:state:queries:0:state:data:json:modules:176","$46:props:state:queries:0:state:data:json:modules:177","$46:props:state:queries:0:state:data:json:modules:178"]},{"title":"Chapter 26: Bayesian Inference","order":36,"modules":["$46:props:state:queries:0:state:data:json:modules:179","$46:props:state:queries:0:state:data:json:modules:180","$46:props:state:queries:0:state:data:json:modules:181","$46:props:state:queries:0:state:data:json:modules:182","$46:props:state:queries:0:state:data:json:modules:183","$46:props:state:queries:0:state:data:json:modules:184","$46:props:state:queries:0:state:data:json:modules:185"]},{"title":"Chapter 27: Graphical Models","order":37,"modules":["$46:props:state:queries:0:state:data:json:modules:186","$46:props:state:queries:0:state:data:json:modules:187","$46:props:state:queries:0:state:data:json:modules:188","$46:props:state:queries:0:state:data:json:modules:189","$46:props:state:queries:0:state:data:json:modules:190","$46:props:state:queries:0:state:data:json:modules:191"]},{"title":"Chapter 28: Gaussian Processes","order":38,"modules":["$46:props:state:queries:0:state:data:json:modules:192","$46:props:state:queries:0:state:data:json:modules:193","$46:props:state:queries:0:state:data:json:modules:194","$46:props:state:queries:0:state:data:json:modules:195","$46:props:state:queries:0:state:data:json:modules:196","$46:props:state:queries:0:state:data:json:modules:197"]},{"title":"Chapter 29: Variational Inference","order":39,"modules":["$46:props:state:queries:0:state:data:json:modules:198","$46:props:state:queries:0:state:data:json:modules:199","$46:props:state:queries:0:state:data:json:modules:200","$46:props:state:queries:0:state:data:json:modules:201","$46:props:state:queries:0:state:data:json:modules:202","$46:props:state:queries:0:state:data:json:modules:203"]},{"title":"Chapter 30: Neural Network Foundations","order":40,"modules":["$46:props:state:queries:0:state:data:json:modules:204","$46:props:state:queries:0:state:data:json:modules:205","$46:props:state:queries:0:state:data:json:modules:206","$46:props:state:queries:0:state:data:json:modules:207","$46:props:state:queries:0:state:data:json:modules:208","$46:props:state:queries:0:state:data:json:modules:209","$46:props:state:queries:0:state:data:json:modules:210","$46:props:state:queries:0:state:data:json:modules:211"]},{"title":"Chapter 31: Backpropagation And Optimization","order":41,"modules":["$46:props:state:queries:0:state:data:json:modules:212","$46:props:state:queries:0:state:data:json:modules:213","$46:props:state:queries:0:state:data:json:modules:214","$46:props:state:queries:0:state:data:json:modules:215"]},{"title":"Chapter 31: Backpropagation Optimization","order":42,"modules":["$46:props:state:queries:0:state:data:json:modules:216","$46:props:state:queries:0:state:data:json:modules:217","$46:props:state:queries:0:state:data:json:modules:218","$46:props:state:queries:0:state:data:json:modules:219","$46:props:state:queries:0:state:data:json:modules:220"]},{"title":"Chapter 32: Regularization In Deep Learning","order":43,"modules":["$46:props:state:queries:0:state:data:json:modules:221","$46:props:state:queries:0:state:data:json:modules:222","$46:props:state:queries:0:state:data:json:modules:223","$46:props:state:queries:0:state:data:json:modules:224","$46:props:state:queries:0:state:data:json:modules:225","$46:props:state:queries:0:state:data:json:modules:226"]},{"title":"Chapter 33: Convolutional Neural Networks","order":44,"modules":["$46:props:state:queries:0:state:data:json:modules:227","$46:props:state:queries:0:state:data:json:modules:228","$46:props:state:queries:0:state:data:json:modules:229","$46:props:state:queries:0:state:data:json:modules:230","$46:props:state:queries:0:state:data:json:modules:231","$46:props:state:queries:0:state:data:json:modules:232","$46:props:state:queries:0:state:data:json:modules:233","$46:props:state:queries:0:state:data:json:modules:234"]},{"title":"Chapter 34: Recurrent Neural Networks","order":45,"modules":["$46:props:state:queries:0:state:data:json:modules:235","$46:props:state:queries:0:state:data:json:modules:236","$46:props:state:queries:0:state:data:json:modules:237","$46:props:state:queries:0:state:data:json:modules:238","$46:props:state:queries:0:state:data:json:modules:239","$46:props:state:queries:0:state:data:json:modules:240","$46:props:state:queries:0:state:data:json:modules:241","$46:props:state:queries:0:state:data:json:modules:242"]},{"title":"Chapter 35: Attention And Transformers","order":46,"modules":["$46:props:state:queries:0:state:data:json:modules:243","$46:props:state:queries:0:state:data:json:modules:244","$46:props:state:queries:0:state:data:json:modules:245","$46:props:state:queries:0:state:data:json:modules:246","$46:props:state:queries:0:state:data:json:modules:247","$46:props:state:queries:0:state:data:json:modules:248","$46:props:state:queries:0:state:data:json:modules:249"]},{"title":"Chapter 35: Attention Transformers","order":47,"modules":["$46:props:state:queries:0:state:data:json:modules:250"]},{"title":"Chapter 36: Generative Models","order":48,"modules":["$46:props:state:queries:0:state:data:json:modules:251","$46:props:state:queries:0:state:data:json:modules:252","$46:props:state:queries:0:state:data:json:modules:253","$46:props:state:queries:0:state:data:json:modules:254","$46:props:state:queries:0:state:data:json:modules:255"]},{"title":"Chapter 37: Semi Supervised And Self Supervised Learning","order":49,"modules":["$46:props:state:queries:0:state:data:json:modules:256"]},{"title":"Chapter 37: Semi Supervised Self Supervised Learning","order":50,"modules":["$46:props:state:queries:0:state:data:json:modules:257","$46:props:state:queries:0:state:data:json:modules:258","$46:props:state:queries:0:state:data:json:modules:259","$46:props:state:queries:0:state:data:json:modules:260","$46:props:state:queries:0:state:data:json:modules:261"]},{"title":"Chapter 38: Transfer Learning And Domain Adaptation","order":51,"modules":["$46:props:state:queries:0:state:data:json:modules:262"]},{"title":"Chapter 38: Transfer Learning Domain Adaptation","order":52,"modules":["$46:props:state:queries:0:state:data:json:modules:263","$46:props:state:queries:0:state:data:json:modules:264","$46:props:state:queries:0:state:data:json:modules:265","$46:props:state:queries:0:state:data:json:modules:266","$46:props:state:queries:0:state:data:json:modules:267"]},{"title":"Chapter 39: Reinforcement Learning","order":53,"modules":["$46:props:state:queries:0:state:data:json:modules:268","$46:props:state:queries:0:state:data:json:modules:269","$46:props:state:queries:0:state:data:json:modules:270","$46:props:state:queries:0:state:data:json:modules:271","$46:props:state:queries:0:state:data:json:modules:272","$46:props:state:queries:0:state:data:json:modules:273","$46:props:state:queries:0:state:data:json:modules:274","$46:props:state:queries:0:state:data:json:modules:275"]},{"title":"Chapter 40: Recommendation Systems","order":54,"modules":["$46:props:state:queries:0:state:data:json:modules:276","$46:props:state:queries:0:state:data:json:modules:277","$46:props:state:queries:0:state:data:json:modules:278","$46:props:state:queries:0:state:data:json:modules:279","$46:props:state:queries:0:state:data:json:modules:280","$46:props:state:queries:0:state:data:json:modules:281"]},{"title":"Chapter 41: Feature Engineering And Selection","order":55,"modules":["$46:props:state:queries:0:state:data:json:modules:282","$46:props:state:queries:0:state:data:json:modules:283","$46:props:state:queries:0:state:data:json:modules:284","$46:props:state:queries:0:state:data:json:modules:285","$46:props:state:queries:0:state:data:json:modules:286","$46:props:state:queries:0:state:data:json:modules:287"]},{"title":"Chapter 41: Feature Engineering Selection","order":56,"modules":["$46:props:state:queries:0:state:data:json:modules:288","$46:props:state:queries:0:state:data:json:modules:289","$46:props:state:queries:0:state:data:json:modules:290","$46:props:state:queries:0:state:data:json:modules:291","$46:props:state:queries:0:state:data:json:modules:292","$46:props:state:queries:0:state:data:json:modules:293","$46:props:state:queries:0:state:data:json:modules:294","$46:props:state:queries:0:state:data:json:modules:295"]},{"title":"Chapter 42: Automl Neural Architecture Search","order":57,"modules":["$46:props:state:queries:0:state:data:json:modules:296","$46:props:state:queries:0:state:data:json:modules:297","$46:props:state:queries:0:state:data:json:modules:298","$46:props:state:queries:0:state:data:json:modules:299","$46:props:state:queries:0:state:data:json:modules:300","$46:props:state:queries:0:state:data:json:modules:301"]},{"title":"Chapter 43: Ml Interpretability And Fairness","order":58,"modules":["$46:props:state:queries:0:state:data:json:modules:302"]},{"title":"Chapter 43: Ml Interpretability Fairness","order":59,"modules":["$46:props:state:queries:0:state:data:json:modules:303","$46:props:state:queries:0:state:data:json:modules:304","$46:props:state:queries:0:state:data:json:modules:305","$46:props:state:queries:0:state:data:json:modules:306","$46:props:state:queries:0:state:data:json:modules:307"]},{"title":"Chapter 44: Research Frontiers","order":60,"modules":["$46:props:state:queries:0:state:data:json:modules:308","$46:props:state:queries:0:state:data:json:modules:309","$46:props:state:queries:0:state:data:json:modules:310","$46:props:state:queries:0:state:data:json:modules:311","$46:props:state:queries:0:state:data:json:modules:312","$46:props:state:queries:0:state:data:json:modules:313"]},{"title":"Chapter 45: Ml Engineering Career","order":61,"modules":["$46:props:state:queries:0:state:data:json:modules:314","$46:props:state:queries:0:state:data:json:modules:315","$46:props:state:queries:0:state:data:json:modules:316","$46:props:state:queries:0:state:data:json:modules:317","$46:props:state:queries:0:state:data:json:modules:318","$46:props:state:queries:0:state:data:json:modules:319","$46:props:state:queries:0:state:data:json:modules:320","$46:props:state:queries:0:state:data:json:modules:321","$46:props:state:queries:0:state:data:json:modules:322"]},{"title":"Applied ML engineering: validate data releases","order":62,"modules":["$46:props:state:queries:0:state:data:json:modules:323","$46:props:state:queries:0:state:data:json:modules:324","$46:props:state:queries:0:state:data:json:modules:325","$46:props:state:queries:0:state:data:json:modules:326","$46:props:state:queries:0:state:data:json:modules:327"]}]},"meta":{"values":{"createdAt":["Date"],"updatedAt":["Date"],"modules.0.createdAt":["Date"],"modules.0.updatedAt":["Date"],"modules.1.createdAt":["Date"],"modules.1.updatedAt":["Date"],"modules.2.createdAt":["Date"],"modules.2.updatedAt":["Date"],"modules.3.createdAt":["Date"],"modules.3.updatedAt":["Date"],"modules.4.createdAt":["Date"],"modules.4.updatedAt":["Date"],"modules.5.createdAt":["Date"],"modules.5.updatedAt":["Date"],"modules.6.createdAt":["Date"],"modules.6.updatedAt":["Date"],"modules.7.createdAt":["Date"],"modules.7.updatedAt":["Date"],"modules.8.createdAt":["Date"],"modules.8.updatedAt":["Date"],"modules.9.createdAt":["Date"],"modules.9.updatedAt":["Date"],"modules.10.createdAt":["Date"],"modules.10.updatedAt":["Date"],"modules.11.createdAt":["Date"],"modules.11.updatedAt":["Date"],"modules.12.createdAt":["Date"],"modules.12.updatedAt":["Date"],"modules.13.createdAt":["Date"],"modules.13.updatedAt":["Date"],"modules.14.createdAt":["Date"],"modules.14.updatedAt":["Date"],"modules.15.createdAt":["Date"],"modules.15.updatedAt":["Date"],"modules.16.createdAt":["Date"],"modules.16.updatedAt":["Date"],"modules.17.createdAt":["Date"],"modules.17.updatedAt":["Date"],"modules.18.createdAt":["Date"],"modules.18.updatedAt":["Date"],"modules.19.createdAt":["Date"],"modules.19.updatedAt":["Date"],"modules.20.createdAt":["Date"],"modules.20.updatedAt":["Date"],"modules.21.createdAt":["Date"],"modules.21.updatedAt":["Date"],"modules.22.createdAt":["Date"],"modules.22.updatedAt":["Date"],"modules.23.createdAt":["Date"],"modules.23.updatedAt":["Date"],"modules.24.createdAt":["Date"],"modules.24.updatedAt":["Date"],"modules.25.createdAt":["Date"],"modules.25.updatedAt":["Date"],"modules.26.createdAt":["Date"],"modules.26.updatedAt":["Date"],"modules.27.createdAt":["Date"],"modules.27.updatedAt":["Date"],"modules.28.createdAt":["Date"],"modules.28.updatedAt":["Date"],"modules.29.createdAt":["Date"],"modules.29.updatedAt":["Date"],"modules.30.createdAt":["Date"],"modules.30.updatedAt":["Date"],"modules.31.createdAt":["Date"],"modules.31.updatedAt":["Date"],"modules.32.createdAt":["Date"],"modules.32.updatedAt":["Date"],"modules.33.createdAt":["Date"],"modules.33.updatedAt":["Date"],"modules.34.createdAt":["Date"],"modules.34.updatedAt":["Date"],"modules.35.createdAt":["Date"],"modules.35.updatedAt":["Date"],"modules.36.createdAt":["Date"],"modules.36.updatedAt":["Date"],"modules.37.createdAt":["Date"],"modules.37.updatedAt":["Date"],"modules.38.createdAt":["Date"],"modules.38.updatedAt":["Date"],"modules.39.createdAt":["Date"],"modules.39.updatedAt":["Date"],"modules.40.createdAt":["Date"],"modules.40.updatedAt":["Date"],"modules.41.createdAt":["Date"],"modules.41.updatedAt":["Date"],"modules.42.createdAt":["Date"],"modules.42.updatedAt":["Date"],"modules.43.createdAt":["Date"],"modules.43.updatedAt":["Date"],"modules.44.createdAt":["Date"],"modules.44.updatedAt":["Date"],"modules.45.createdAt":["Date"],"modules.45.updatedAt":["Date"],"modules.46.createdAt":["Date"],"modules.46.updatedAt":["Date"],"modules.47.createdAt":["Date"],"modules.47.updatedAt":["Date"],"modules.48.createdAt":["Date"],"modules.48.updatedAt":["Date"],"modules.49.createdAt":["Date"],"modules.49.updatedAt":["Date"],"modules.50.createdAt":["Date"],"modules.50.updatedAt":["Date"],"modules.51.createdAt":["Date"],"modules.51.updatedAt":["Date"],"modules.52.createdAt":["Date"],"modules.52.updatedAt":["Date"],"modules.53.createdAt":["Date"],"modules.53.updatedAt":["Date"],"modules.54.createdAt":["Date"],"modules.54.updatedAt":["Date"],"modules.55.createdAt":["Date"],"modules.55.updatedAt":["Date"],"modules.56.createdAt":["Date"],"modules.56.updatedAt":["Date"],"modules.57.createdAt":["Date"],"modules.57.updatedAt":["Date"],"modules.58.createdAt":["Date"],"modules.58.updatedAt":["Date"],"modules.59.createdAt":["Date"],"modules.59.updatedAt":["Date"],"modules.60.createdAt":["Date"],"modules.60.updatedAt":["Date"],"modules.61.createdAt":["Date"],"modules.61.updatedAt":["Date"],"modules.62.createdAt":["Date"],"modules.62.updatedAt":["Date"],"modules.63.createdAt":["Date"],"modules.63.updatedAt":["Date"],"modules.64.createdAt":["Date"],"modules.64.updatedAt":["Date"],"modules.65.createdAt":["Date"],"modules.65.updatedAt":["Date"],"modules.66.createdAt":["Date"],"modules.66.updatedAt":["Date"],"modules.67.createdAt":["Date"],"modules.67.updatedAt":["Date"],"modules.68.createdAt":["Date"],"modules.68.updatedAt":["Date"],"modules.69.createdAt":["Date"],"modules.69.updatedAt":["Date"],"modules.70.createdAt":["Date"],"modules.70.updatedAt":["Date"],"modules.71.createdAt":["Date"],"modules.71.updatedAt":["Date"],"modules.72.createdAt":["Date"],"modules.72.updatedAt":["Date"],"modules.73.createdAt":["Date"],"modules.73.updatedAt":["Date"],"modules.74.createdAt":["Date"],"modules.74.updatedAt":["Date"],"modules.75.createdAt":["Date"],"modules.75.updatedAt":["Date"],"modules.76.createdAt":["Date"],"modules.76.updatedAt":["Date"],"modules.77.createdAt":["Date"],"modules.77.updatedAt":["Date"],"modules.78.createdAt":["Date"],"modules.78.updatedAt":["Date"],"modules.79.createdAt":["Date"],"modules.79.updatedAt":["Date"],"modules.80.createdAt":["Date"],"modules.80.updatedAt":["Date"],"modules.81.createdAt":["Date"],"modules.81.updatedAt":["Date"],"modules.82.createdAt":["Date"],"modules.82.updatedAt":["Date"],"modules.83.createdAt":["Date"],"modules.83.updatedAt":["Date"],"modules.84.createdAt":["Date"],"modules.84.updatedAt":["Date"],"modules.85.createdAt":["Date"],"modules.85.updatedAt":["Date"],"modules.86.createdAt":["Date"],"modules.86.updatedAt":["Date"],"modules.87.createdAt":["Date"],"modules.87.updatedAt":["Date"],"modules.88.createdAt":["Date"],"modules.88.updatedAt":["Date"],"modules.89.createdAt":["Date"],"modules.89.updatedAt":["Date"],"modules.90.createdAt":["Date"],"modules.90.updatedAt":["Date"],"modules.91.createdAt":["Date"],"modules.91.updatedAt":["Date"],"modules.92.createdAt":["Date"],"modules.92.updatedAt":["Date"],"modules.93.createdAt":["Date"],"modules.93.updatedAt":["Date"],"modules.94.createdAt":["Date"],"modules.94.updatedAt":["Date"],"modules.95.createdAt":["Date"],"modules.95.updatedAt":["Date"],"modules.96.createdAt":["Date"],"modules.96.updatedAt":["Date"],"modules.97.createdAt":["Date"],"modules.97.updatedAt":["Date"],"modules.98.createdAt":["Date"],"modules.98.updatedAt":["Date"],"modules.99.createdAt":["Date"],"modules.99.updatedAt":["Date"],"modules.100.createdAt":["Date"],"modules.100.updatedAt":["Date"],"modules.101.createdAt":["Date"],"modules.101.updatedAt":["Date"],"modules.102.createdAt":["Date"],"modules.102.updatedAt":["Date"],"modules.103.createdAt":["Date"],"modules.103.updatedAt":["Date"],"modules.104.createdAt":["Date"],"modules.104.updatedAt":["Date"],"modules.105.createdAt":["Date"],"modules.105.updatedAt":["Date"],"modules.106.createdAt":["Date"],"modules.106.updatedAt":["Date"],"modules.107.createdAt":["Date"],"modules.107.updatedAt":["Date"],"modules.108.createdAt":["Date"],"modules.108.updatedAt":["Date"],"modules.109.createdAt":["Date"],"modules.109.updatedAt":["Date"],"modules.110.createdAt":["Date"],"modules.110.updatedAt":["Date"],"modules.111.createdAt":["Date"],"modules.111.updatedAt":["Date"],"modules.112.createdAt":["Date"],"modules.112.updatedAt":["Date"],"modules.113.createdAt":["Date"],"modules.113.updatedAt":["Date"],"modules.114.createdAt":["Date"],"modules.114.updatedAt":["Date"],"modules.115.createdAt":["Date"],"modules.115.updatedAt":["Date"],"modules.116.createdAt":["Date"],"modules.116.updatedAt":["Date"],"modules.117.createdAt":["Date"],"modules.117.updatedAt":["Date"],"modules.118.createdAt":["Date"],"modules.118.updatedAt":["Date"],"modules.119.createdAt":["Date"],"modules.119.updatedAt":["Date"],"modules.120.createdAt":["Date"],"modules.120.updatedAt":["Date"],"modules.121.createdAt":["Date"],"modules.121.updatedAt":["Date"],"modules.122.createdAt":["Date"],"modules.122.updatedAt":["Date"],"modules.123.createdAt":["Date"],"modules.123.updatedAt":["Date"],"modules.124.createdAt":["Date"],"modules.124.updatedAt":["Date"],"modules.125.createdAt":["Date"],"modules.125.updatedAt":["Date"],"modules.126.createdAt":["Date"],"modules.126.updatedAt":["Date"],"modules.127.createdAt":["Date"],"modules.127.updatedAt":["Date"],"modules.128.createdAt":["Date"],"modules.128.updatedAt":["Date"],"modules.129.createdAt":["Date"],"modules.129.updatedAt":["Date"],"modules.130.createdAt":["Date"],"modules.130.updatedAt":["Date"],"modules.131.createdAt":["Date"],"modules.131.updatedAt":["Date"],"modules.132.createdAt":["Date"],"modules.132.updatedAt":["Date"],"modules.133.createdAt":["Date"],"modules.133.updatedAt":["Date"],"modules.134.createdAt":["Date"],"modules.134.updatedAt":["Date"],"modules.135.createdAt":["Date"],"modules.135.updatedAt":["Date"],"modules.136.createdAt":["Date"],"modules.136.updatedAt":["Date"],"modules.137.createdAt":["Date"],"modules.137.updatedAt":["Date"],"modules.138.createdAt":["Date"],"modules.138.updatedAt":["Date"],"modules.139.createdAt":["Date"],"modules.139.updatedAt":["Date"],"modules.140.createdAt":["Date"],"modules.140.updatedAt":["Date"],"modules.141.createdAt":["Date"],"modules.141.updatedAt":["Date"],"modules.142.createdAt":["Date"],"modules.142.updatedAt":["Date"],"modules.143.createdAt":["Date"],"modules.143.updatedAt":["Date"],"modules.144.createdAt":["Date"],"modules.144.updatedAt":["Date"],"modules.145.createdAt":["Date"],"modules.145.updatedAt":["Date"],"modules.146.createdAt":["Date"],"modules.146.updatedAt":["Date"],"modules.147.createdAt":["Date"],"modules.147.updatedAt":["Date"],"modules.148.createdAt":["Date"],"modules.148.updatedAt":["Date"],"modules.149.createdAt":["Date"],"modules.149.updatedAt":["Date"],"modules.150.createdAt":["Date"],"modules.150.updatedAt":["Date"],"modules.151.createdAt":["Date"],"modules.151.updatedAt":["Date"],"modules.152.createdAt":["Date"],"modules.152.updatedAt":["Date"],"modules.153.createdAt":["Date"],"modules.153.updatedAt":["Date"],"modules.154.createdAt":["Date"],"modules.154.updatedAt":["Date"],"modules.155.createdAt":["Date"],"modules.155.updatedAt":["Date"],"modules.156.createdAt":["Date"],"modules.156.updatedAt":["Date"],"modules.157.createdAt":["Date"],"modules.157.updatedAt":["Date"],"modules.158.createdAt":["Date"],"modules.158.updatedAt":["Date"],"modules.159.createdAt":["Date"],"modules.159.updatedAt":["Date"],"modules.160.createdAt":["Date"],"modules.160.updatedAt":["Date"],"modules.161.createdAt":["Date"],"modules.161.updatedAt":["Date"],"modules.162.createdAt":["Date"],"modules.162.updatedAt":["Date"],"modules.163.createdAt":["Date"],"modules.163.updatedAt":["Date"],"modules.164.createdAt":["Date"],"modules.164.updatedAt":["Date"],"modules.165.createdAt":["Date"],"modules.165.updatedAt":["Date"],"modules.166.createdAt":["Date"],"modules.166.updatedAt":["Date"],"modules.167.createdAt":["Date"],"modules.167.updatedAt":["Date"],"modules.168.createdAt":["Date"],"modules.168.updatedAt":["Date"],"modules.169.createdAt":["Date"],"modules.169.updatedAt":["Date"],"modules.170.createdAt":["Date"],"modules.170.updatedAt":["Date"],"modules.171.createdAt":["Date"],"modules.171.updatedAt":["Date"],"modules.172.createdAt":["Date"],"modules.172.updatedAt":["Date"],"modules.173.createdAt":["Date"],"modules.173.updatedAt":["Date"],"modules.174.createdAt":["Date"],"modules.174.updatedAt":["Date"],"modules.175.createdAt":["Date"],"modules.175.updatedAt":["Date"],"modules.176.createdAt":["Date"],"modules.176.updatedAt":["Date"],"modules.177.createdAt":["Date"],"modules.177.updatedAt":["Date"],"modules.178.createdAt":["Date"],"modules.178.updatedAt":["Date"],"modules.179.createdAt":["Date"],"modules.179.updatedAt":["Date"],"modules.180.createdAt":["Date"],"modules.180.updatedAt":["Date"],"modules.181.createdAt":["Date"],"modules.181.updatedAt":["Date"],"modules.182.createdAt":["Date"],"modules.182.updatedAt":["Date"],"modules.183.createdAt":["Date"],"modules.183.updatedAt":["Date"],"modules.184.createdAt":["Date"],"modules.184.updatedAt":["Date"],"modules.185.createdAt":["Date"],"modules.185.updatedAt":["Date"],"modules.186.createdAt":["Date"],"modules.186.updatedAt":["Date"],"modules.187.createdAt":["Date"],"modules.187.updatedAt":["Date"],"modules.188.createdAt":["Date"],"modules.188.updatedAt":["Date"],"modules.189.createdAt":["Date"],"modules.189.updatedAt":["Date"],"modules.190.createdAt":["Date"],"modules.190.updatedAt":["Date"],"modules.191.createdAt":["Date"],"modules.191.updatedAt":["Date"],"modules.192.createdAt":["Date"],"modules.192.updatedAt":["Date"],"modules.193.createdAt":["Date"],"modules.193.updatedAt":["Date"],"modules.194.createdAt":["Date"],"modules.194.updatedAt":["Date"],"modules.195.createdAt":["Date"],"modules.195.updatedAt":["Date"],"modules.196.createdAt":["Date"],"modules.196.updatedAt":["Date"],"modules.197.createdAt":["Date"],"modules.197.updatedAt":["Date"],"modules.198.createdAt":["Date"],"modules.198.updatedAt":["Date"],"modules.199.createdAt":["Date"],"modules.199.updatedAt":["Date"],"modules.200.createdAt":["Date"],"modules.200.updatedAt":["Date"],"modules.201.createdAt":["Date"],"modules.201.updatedAt":["Date"],"modules.202.createdAt":["Date"],"modules.202.updatedAt":["Date"],"modules.203.createdAt":["Date"],"modules.203.updatedAt":["Date"],"modules.204.createdAt":["Date"],"modules.204.updatedAt":["Date"],"modules.205.createdAt":["Date"],"modules.205.updatedAt":["Date"],"modules.206.createdAt":["Date"],"modules.206.updatedAt":["Date"],"modules.207.createdAt":["Date"],"modules.207.updatedAt":["Date"],"modules.208.createdAt":["Date"],"modules.208.updatedAt":["Date"],"modules.209.createdAt":["Date"],"modules.209.updatedAt":["Date"],"modules.210.createdAt":["Date"],"modules.210.updatedAt":["Date"],"modules.211.createdAt":["Date"],"modules.211.updatedAt":["Date"],"modules.212.createdAt":["Date"],"modules.212.updatedAt":["Date"],"modules.213.createdAt":["Date"],"modules.213.updatedAt":["Date"],"modules.214.createdAt":["Date"],"modules.214.updatedAt":["Date"],"modules.215.createdAt":["Date"],"modules.215.updatedAt":["Date"],"modules.216.createdAt":["Date"],"modules.216.updatedAt":["Date"],"modules.217.createdAt":["Date"],"modules.217.updatedAt":["Date"],"modules.218.createdAt":["Date"],"modules.218.updatedAt":["Date"],"modules.219.createdAt":["Date"],"modules.219.updatedAt":["Date"],"modules.220.createdAt":["Date"],"modules.220.updatedAt":["Date"],"modules.221.createdAt":["Date"],"modules.221.updatedAt":["Date"],"modules.222.createdAt":["Date"],"modules.222.updatedAt":["Date"],"modules.223.createdAt":["Date"],"modules.223.updatedAt":["Date"],"modules.224.createdAt":["Date"],"modules.224.updatedAt":["Date"],"modules.225.createdAt":["Date"],"modules.225.updatedAt":["Date"],"modules.226.createdAt":["Date"],"modules.226.updatedAt":["Date"],"modules.227.createdAt":["Date"],"modules.227.updatedAt":["Date"],"modules.228.createdAt":["Date"],"modules.228.updatedAt":["Date"],"modules.229.createdAt":["Date"],"modules.229.updatedAt":["Date"],"modules.230.createdAt":["Date"],"modules.230.updatedAt":["Date"],"modules.231.createdAt":["Date"],"modules.231.updatedAt":["Date"],"modules.232.createdAt":["Date"],"modules.232.updatedAt":["Date"],"modules.233.createdAt":["Date"],"modules.233.updatedAt":["Date"],"modules.234.createdAt":["Date"],"modules.234.updatedAt":["Date"],"modules.235.createdAt":["Date"],"modules.235.updatedAt":["Date"],"modules.236.createdAt":["Date"],"modules.236.updatedAt":["Date"],"modules.237.createdAt":["Date"],"modules.237.updatedAt":["Date"],"modules.238.createdAt":["Date"],"modules.238.updatedAt":["Date"],"modules.239.createdAt":["Date"],"modules.239.updatedAt":["Date"],"modules.240.createdAt":["Date"],"modules.240.updatedAt":["Date"],"modules.241.createdAt":["Date"],"modules.241.updatedAt":["Date"],"modules.242.createdAt":["Date"],"modules.242.updatedAt":["Date"],"modules.243.createdAt":["Date"],"modules.243.updatedAt":["Date"],"modules.244.createdAt":["Date"],"modules.244.updatedAt":["Date"],"modules.245.createdAt":["Date"],"modules.245.updatedAt":["Date"],"modules.246.createdAt":["Date"],"modules.246.updatedAt":["Date"],"modules.247.createdAt":["Date"],"modules.247.updatedAt":["Date"],"modules.248.createdAt":["Date"],"modules.248.updatedAt":["Date"],"modules.249.createdAt":["Date"],"modules.249.updatedAt":["Date"],"modules.250.createdAt":["Date"],"modules.250.updatedAt":["Date"],"modules.251.createdAt":["Date"],"modules.251.updatedAt":["Date"],"modules.252.createdAt":["Date"],"modules.252.updatedAt":["Date"],"modules.253.createdAt":["Date"],"modules.253.updatedAt":["Date"],"modules.254.createdAt":["Date"],"modules.254.updatedAt":["Date"],"modules.255.createdAt":["Date"],"modules.255.updatedAt":["Date"],"modules.256.createdAt":["Date"],"modules.256.updatedAt":["Date"],"modules.257.createdAt":["Date"],"modules.257.updatedAt":["Date"],"modules.258.createdAt":["Date"],"modules.258.updatedAt":["Date"],"modules.259.createdAt":["Date"],"modules.259.updatedAt":["Date"],"modules.260.createdAt":["Date"],"modules.260.updatedAt":["Date"],"modules.261.createdAt":["Date"],"modules.261.updatedAt":["Date"],"modules.262.createdAt":["Date"],"modules.262.updatedAt":["Date"],"modules.263.createdAt":["Date"],"modules.263.updatedAt":["Date"],"modules.264.createdAt":["Date"],"modules.264.updatedAt":["Date"],"modules.265.createdAt":["Date"],"modules.265.updatedAt":["Date"],"modules.266.createdAt":["Date"],"modules.266.updatedAt":["Date"],"modules.267.createdAt":["Date"],"modules.267.updatedAt":["Date"],"modules.268.createdAt":["Date"],"modules.268.updatedAt":["Date"],"modules.269.createdAt":["Date"],"modules.269.updatedAt":["Date"],"modules.270.createdAt":["Date"],"modules.270.updatedAt":["Date"],"modules.271.createdAt":["Date"],"modules.271.updatedAt":["Date"],"modules.272.createdAt":["Date"],"modules.272.updatedAt":["Date"],"modules.273.createdAt":["Date"],"modules.273.updatedAt":["Date"],"modules.274.createdAt":["Date"],"modules.274.updatedAt":["Date"],"modules.275.createdAt":["Date"],"modules.275.updatedAt":["Date"],"modules.276.createdAt":["Date"],"modules.276.updatedAt":["Date"],"modules.277.createdAt":["Date"],"modules.277.updatedAt":["Date"],"modules.278.createdAt":["Date"],"modules.278.updatedAt":["Date"],"modules.279.createdAt":["Date"],"modules.279.updatedAt":["Date"],"modules.280.createdAt":["Date"],"modules.280.updatedAt":["Date"],"modules.281.createdAt":["Date"],"modules.281.updatedAt":["Date"],"modules.282.createdAt":["Date"],"modules.282.updatedAt":["Date"],"modules.283.createdAt":["Date"],"modules.283.updatedAt":["Date"],"modules.284.createdAt":["Date"],"modules.284.updatedAt":["Date"],"modules.285.createdAt":["Date"],"modules.285.updatedAt":["Date"],"modules.286.createdAt":["Date"],"modules.286.updatedAt":["Date"],"modules.287.createdAt":["Date"],"modules.287.updatedAt":["Date"],"modules.288.createdAt":["Date"],"modules.288.updatedAt":["Date"],"modules.289.createdAt":["Date"],"modules.289.updatedAt":["Date"],"modules.290.createdAt":["Date"],"modules.290.updatedAt":["Date"],"modules.291.createdAt":["Date"],"modules.291.updatedAt":["Date"],"modules.292.createdAt":["Date"],"modules.292.updatedAt":["Date"],"modules.293.createdAt":["Date"],"modules.293.updatedAt":["Date"],"modules.294.createdAt":["Date"],"modules.294.updatedAt":["Date"],"modules.295.createdAt":["Date"],"modules.295.updatedAt":["Date"],"modules.296.createdAt":["Date"],"modules.296.updatedAt":["Date"],"modules.297.createdAt":["Date"],"modules.297.updatedAt":["Date"],"modules.298.createdAt":["Date"],"modules.298.updatedAt":["Date"],"modules.299.createdAt":["Date"],"modules.299.updatedAt":["Date"],"modules.300.createdAt":["Date"],"modules.300.updatedAt":["Date"],"modules.301.createdAt":["Date"],"modules.301.updatedAt":["Date"],"modules.302.createdAt":["Date"],"modules.302.updatedAt":["Date"],"modules.303.createdAt":["Date"],"modules.303.updatedAt":["Date"],"modules.304.createdAt":["Date"],"modules.304.updatedAt":["Date"],"modules.305.createdAt":["Date"],"modules.305.updatedAt":["Date"],"modules.306.createdAt":["Date"],"modules.306.updatedAt":["Date"],"modules.307.createdAt":["Date"],"modules.307.updatedAt":["Date"],"modules.308.createdAt":["Date"],"modules.308.updatedAt":["Date"],"modules.309.createdAt":["Date"],"modules.309.updatedAt":["Date"],"modules.310.createdAt":["Date"],"modules.310.updatedAt":["Date"],"modules.311.createdAt":["Date"],"modules.311.updatedAt":["Date"],"modules.312.createdAt":["Date"],"modules.312.updatedAt":["Date"],"modules.313.createdAt":["Date"],"modules.313.updatedAt":["Date"],"modules.314.createdAt":["Date"],"modules.314.updatedAt":["Date"],"modules.315.createdAt":["Date"],"modules.315.updatedAt":["Date"],"modules.316.createdAt":["Date"],"modules.316.updatedAt":["Date"],"modules.317.createdAt":["Date"],"modules.317.updatedAt":["Date"],"modules.318.createdAt":["Date"],"modules.318.updatedAt":["Date"],"modules.319.createdAt":["Date"],"modules.319.updatedAt":["Date"],"modules.320.createdAt":["Date"],"modules.320.updatedAt":["Date"],"modules.321.createdAt":["Date"],"modules.321.updatedAt":["Date"],"modules.322.createdAt":["Date"],"modules.322.updatedAt":["Date"],"modules.323.createdAt":["Date"],"modules.323.updatedAt":["Date"],"modules.324.createdAt":["Date"],"modules.324.updatedAt":["Date"],"modules.325.createdAt":["Date"],"modules.325.updatedAt":["Date"],"modules.326.createdAt":["Date"],"modules.326.updatedAt":["Date"],"modules.327.createdAt":["Date"],"modules.327.updatedAt":["Date"],"sections.0.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.0.createdAt","sections.0.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.0.updatedAt","sections.0.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.1.createdAt","sections.0.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.1.updatedAt","sections.0.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.2.createdAt","sections.0.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.2.updatedAt","sections.0.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.3.createdAt","sections.0.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.3.updatedAt","sections.1.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.4.createdAt","sections.1.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.4.updatedAt","sections.1.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.5.createdAt","sections.1.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.5.updatedAt","sections.1.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.6.createdAt","sections.1.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.6.updatedAt","sections.2.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.7.createdAt","sections.2.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.7.updatedAt","sections.2.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.8.createdAt","sections.2.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.8.updatedAt","sections.2.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.9.createdAt","sections.2.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.9.updatedAt","sections.2.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.10.createdAt","sections.2.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.10.updatedAt","sections.2.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.11.createdAt","sections.2.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.11.updatedAt","sections.2.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.12.createdAt","sections.2.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.12.updatedAt","sections.2.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.13.createdAt","sections.2.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.13.updatedAt","sections.2.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.14.createdAt","sections.2.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.14.updatedAt","sections.3.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.15.createdAt","sections.3.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.15.updatedAt","sections.3.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.16.createdAt","sections.3.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.16.updatedAt","sections.3.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.17.createdAt","sections.3.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.17.updatedAt","sections.3.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.18.createdAt","sections.3.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.18.updatedAt","sections.3.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.19.createdAt","sections.3.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.19.updatedAt","sections.4.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.20.createdAt","sections.4.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.20.updatedAt","sections.4.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.21.createdAt","sections.4.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.21.updatedAt","sections.4.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.22.createdAt","sections.4.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.22.updatedAt","sections.4.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.23.createdAt","sections.4.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.23.updatedAt","sections.4.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.24.createdAt","sections.4.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.24.updatedAt","sections.4.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.25.createdAt","sections.4.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.25.updatedAt","sections.4.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.26.createdAt","sections.4.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.26.updatedAt","sections.4.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.27.createdAt","sections.4.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.27.updatedAt","sections.4.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.28.createdAt","sections.4.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.28.updatedAt","sections.5.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.29.createdAt","sections.5.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.29.updatedAt","sections.5.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.30.createdAt","sections.5.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.30.updatedAt","sections.5.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.31.createdAt","sections.5.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.31.updatedAt","sections.5.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.32.createdAt","sections.5.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.32.updatedAt","sections.5.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.33.createdAt","sections.5.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.33.updatedAt","sections.5.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.34.createdAt","sections.5.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.34.updatedAt","sections.5.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.35.createdAt","sections.5.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.35.updatedAt","sections.5.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.36.createdAt","sections.5.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.36.updatedAt","sections.6.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.37.createdAt","sections.6.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.37.updatedAt","sections.6.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.38.createdAt","sections.6.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.38.updatedAt","sections.6.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.39.createdAt","sections.6.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.39.updatedAt","sections.6.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.40.createdAt","sections.6.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.40.updatedAt","sections.6.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.41.createdAt","sections.6.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.41.updatedAt","sections.6.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.42.createdAt","sections.6.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.42.updatedAt","sections.7.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.43.createdAt","sections.7.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.43.updatedAt","sections.7.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.44.createdAt","sections.7.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.44.updatedAt","sections.8.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.45.createdAt","sections.8.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.45.updatedAt","sections.8.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.46.createdAt","sections.8.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.46.updatedAt","sections.8.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.47.createdAt","sections.8.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.47.updatedAt","sections.8.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.48.createdAt","sections.8.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.48.updatedAt","sections.8.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.49.createdAt","sections.8.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.49.updatedAt","sections.8.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.50.createdAt","sections.8.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.50.updatedAt","sections.8.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.51.createdAt","sections.8.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.51.updatedAt","sections.9.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.52.createdAt","sections.9.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.52.updatedAt","sections.9.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.53.createdAt","sections.9.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.53.updatedAt","sections.9.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.54.createdAt","sections.9.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.54.updatedAt","sections.9.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.55.createdAt","sections.9.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.55.updatedAt","sections.9.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.56.createdAt","sections.9.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.56.updatedAt","sections.9.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.57.createdAt","sections.9.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.57.updatedAt","sections.10.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.58.createdAt","sections.10.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.58.updatedAt","sections.10.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.59.createdAt","sections.10.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.59.updatedAt","sections.11.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.60.createdAt","sections.11.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.60.updatedAt","sections.11.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.61.createdAt","sections.11.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.61.updatedAt","sections.11.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.62.createdAt","sections.11.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.62.updatedAt","sections.11.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.63.createdAt","sections.11.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.63.updatedAt","sections.11.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.64.createdAt","sections.11.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.64.updatedAt","sections.12.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.65.createdAt","sections.12.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.65.updatedAt","sections.12.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.66.createdAt","sections.12.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.66.updatedAt","sections.12.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.67.createdAt","sections.12.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.67.updatedAt","sections.12.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.68.createdAt","sections.12.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.68.updatedAt","sections.12.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.69.createdAt","sections.12.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.69.updatedAt","sections.12.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.70.createdAt","sections.12.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.70.updatedAt","sections.13.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.71.createdAt","sections.13.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.71.updatedAt","sections.14.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.72.createdAt","sections.14.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.72.updatedAt","sections.14.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.73.createdAt","sections.14.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.73.updatedAt","sections.14.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.74.createdAt","sections.14.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.74.updatedAt","sections.14.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.75.createdAt","sections.14.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.75.updatedAt","sections.14.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.76.createdAt","sections.14.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.76.updatedAt","sections.14.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.77.createdAt","sections.14.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.77.updatedAt","sections.14.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.78.createdAt","sections.14.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.78.updatedAt","sections.14.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.79.createdAt","sections.14.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.79.updatedAt","sections.15.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.80.createdAt","sections.15.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.80.updatedAt","sections.15.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.81.createdAt","sections.15.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.81.updatedAt","sections.15.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.82.createdAt","sections.15.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.82.updatedAt","sections.15.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.83.createdAt","sections.15.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.83.updatedAt","sections.15.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.84.createdAt","sections.15.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.84.updatedAt","sections.15.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.85.createdAt","sections.15.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.85.updatedAt","sections.15.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.86.createdAt","sections.15.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.86.updatedAt","sections.16.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.87.createdAt","sections.16.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.87.updatedAt","sections.16.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.88.createdAt","sections.16.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.88.updatedAt","sections.16.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.89.createdAt","sections.16.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.89.updatedAt","sections.16.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.90.createdAt","sections.16.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.90.updatedAt","sections.16.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.91.createdAt","sections.16.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.91.updatedAt","sections.16.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.92.createdAt","sections.16.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.92.updatedAt","sections.17.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.93.createdAt","sections.17.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.93.updatedAt","sections.17.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.94.createdAt","sections.17.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.94.updatedAt","sections.18.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.95.createdAt","sections.18.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.95.updatedAt","sections.18.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.96.createdAt","sections.18.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.96.updatedAt","sections.18.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.97.createdAt","sections.18.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.97.updatedAt","sections.18.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.98.createdAt","sections.18.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.98.updatedAt","sections.19.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.99.createdAt","sections.19.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.99.updatedAt","sections.19.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.100.createdAt","sections.19.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.100.updatedAt","sections.19.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.101.createdAt","sections.19.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.101.updatedAt","sections.19.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.102.createdAt","sections.19.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.102.updatedAt","sections.19.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.103.createdAt","sections.19.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.103.updatedAt","sections.19.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.104.createdAt","sections.19.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.104.updatedAt","sections.19.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.105.createdAt","sections.19.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.105.updatedAt","sections.19.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.106.createdAt","sections.19.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.106.updatedAt","sections.20.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.107.createdAt","sections.20.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.107.updatedAt","sections.20.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.108.createdAt","sections.20.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.108.updatedAt","sections.20.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.109.createdAt","sections.20.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.109.updatedAt","sections.21.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.110.createdAt","sections.21.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.110.updatedAt","sections.21.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.111.createdAt","sections.21.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.111.updatedAt","sections.21.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.112.createdAt","sections.21.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.112.updatedAt","sections.22.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.113.createdAt","sections.22.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.113.updatedAt","sections.22.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.114.createdAt","sections.22.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.114.updatedAt","sections.22.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.115.createdAt","sections.22.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.115.updatedAt","sections.22.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.116.createdAt","sections.22.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.116.updatedAt","sections.22.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.117.createdAt","sections.22.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.117.updatedAt","sections.22.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.118.createdAt","sections.22.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.118.updatedAt","sections.23.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.119.createdAt","sections.23.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.119.updatedAt","sections.23.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.120.createdAt","sections.23.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.120.updatedAt","sections.23.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.121.createdAt","sections.23.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.121.updatedAt","sections.23.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.122.createdAt","sections.23.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.122.updatedAt","sections.23.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.123.createdAt","sections.23.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.123.updatedAt","sections.23.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.124.createdAt","sections.23.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.124.updatedAt","sections.24.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.125.createdAt","sections.24.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.125.updatedAt","sections.24.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.126.createdAt","sections.24.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.126.updatedAt","sections.24.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.127.createdAt","sections.24.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.127.updatedAt","sections.24.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.128.createdAt","sections.24.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.128.updatedAt","sections.24.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.129.createdAt","sections.24.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.129.updatedAt","sections.24.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.130.createdAt","sections.24.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.130.updatedAt","sections.24.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.131.createdAt","sections.24.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.131.updatedAt","sections.25.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.132.createdAt","sections.25.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.132.updatedAt","sections.25.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.133.createdAt","sections.25.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.133.updatedAt","sections.25.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.134.createdAt","sections.25.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.134.updatedAt","sections.26.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.135.createdAt","sections.26.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.135.updatedAt","sections.26.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.136.createdAt","sections.26.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.136.updatedAt","sections.26.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.137.createdAt","sections.26.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.137.updatedAt","sections.26.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.138.createdAt","sections.26.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.138.updatedAt","sections.27.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.139.createdAt","sections.27.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.139.updatedAt","sections.27.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.140.createdAt","sections.27.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.140.updatedAt","sections.27.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.141.createdAt","sections.27.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.141.updatedAt","sections.27.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.142.createdAt","sections.27.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.142.updatedAt","sections.27.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.143.createdAt","sections.27.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.143.updatedAt","sections.27.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.144.createdAt","sections.27.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.144.updatedAt","sections.28.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.145.createdAt","sections.28.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.145.updatedAt","sections.28.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.146.createdAt","sections.28.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.146.updatedAt","sections.28.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.147.createdAt","sections.28.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.147.updatedAt","sections.28.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.148.createdAt","sections.28.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.148.updatedAt","sections.28.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.149.createdAt","sections.28.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.149.updatedAt","sections.28.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.150.createdAt","sections.28.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.150.updatedAt","sections.28.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.151.createdAt","sections.28.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.151.updatedAt","sections.28.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.152.createdAt","sections.28.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.152.updatedAt","sections.29.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.153.createdAt","sections.29.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.153.updatedAt","sections.29.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.154.createdAt","sections.29.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.154.updatedAt","sections.29.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.155.createdAt","sections.29.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.155.updatedAt","sections.30.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.156.createdAt","sections.30.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.156.updatedAt","sections.30.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.157.createdAt","sections.30.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.157.updatedAt","sections.30.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.158.createdAt","sections.30.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.158.updatedAt","sections.31.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.159.createdAt","sections.31.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.159.updatedAt","sections.31.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.160.createdAt","sections.31.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.160.updatedAt","sections.31.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.161.createdAt","sections.31.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.161.updatedAt","sections.31.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.162.createdAt","sections.31.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.162.updatedAt","sections.31.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.163.createdAt","sections.31.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.163.updatedAt","sections.31.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.164.createdAt","sections.31.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.164.updatedAt","sections.31.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.165.createdAt","sections.31.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.165.updatedAt","sections.31.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.166.createdAt","sections.31.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.166.updatedAt","sections.32.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.167.createdAt","sections.32.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.167.updatedAt","sections.32.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.168.createdAt","sections.32.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.168.updatedAt","sections.32.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.169.createdAt","sections.32.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.169.updatedAt","sections.32.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.170.createdAt","sections.32.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.170.updatedAt","sections.32.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.171.createdAt","sections.32.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.171.updatedAt","sections.32.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.172.createdAt","sections.32.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.172.updatedAt","sections.33.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.173.createdAt","sections.33.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.173.updatedAt","sections.33.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.174.createdAt","sections.33.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.174.updatedAt","sections.33.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.175.createdAt","sections.33.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.175.updatedAt","sections.34.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.176.createdAt","sections.34.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.176.updatedAt","sections.34.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.177.createdAt","sections.34.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.177.updatedAt","sections.34.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.178.createdAt","sections.34.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.178.updatedAt","sections.35.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.179.createdAt","sections.35.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.179.updatedAt","sections.35.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.180.createdAt","sections.35.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.180.updatedAt","sections.35.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.181.createdAt","sections.35.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.181.updatedAt","sections.35.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.182.createdAt","sections.35.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.182.updatedAt","sections.35.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.183.createdAt","sections.35.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.183.updatedAt","sections.35.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.184.createdAt","sections.35.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.184.updatedAt","sections.35.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.185.createdAt","sections.35.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.185.updatedAt","sections.36.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.186.createdAt","sections.36.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.186.updatedAt","sections.36.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.187.createdAt","sections.36.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.187.updatedAt","sections.36.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.188.createdAt","sections.36.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.188.updatedAt","sections.36.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.189.createdAt","sections.36.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.189.updatedAt","sections.36.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.190.createdAt","sections.36.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.190.updatedAt","sections.36.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.191.createdAt","sections.36.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.191.updatedAt","sections.37.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.192.createdAt","sections.37.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.192.updatedAt","sections.37.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.193.createdAt","sections.37.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.193.updatedAt","sections.37.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.194.createdAt","sections.37.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.194.updatedAt","sections.37.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.195.createdAt","sections.37.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.195.updatedAt","sections.37.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.196.createdAt","sections.37.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.196.updatedAt","sections.37.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.197.createdAt","sections.37.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.197.updatedAt","sections.38.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.198.createdAt","sections.38.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.198.updatedAt","sections.38.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.199.createdAt","sections.38.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.199.updatedAt","sections.38.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.200.createdAt","sections.38.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.200.updatedAt","sections.38.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.201.createdAt","sections.38.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.201.updatedAt","sections.38.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.202.createdAt","sections.38.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.202.updatedAt","sections.38.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.203.createdAt","sections.38.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.203.updatedAt","sections.39.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.204.createdAt","sections.39.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.204.updatedAt","sections.39.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.205.createdAt","sections.39.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.205.updatedAt","sections.39.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.206.createdAt","sections.39.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.206.updatedAt","sections.39.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.207.createdAt","sections.39.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.207.updatedAt","sections.39.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.208.createdAt","sections.39.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.208.updatedAt","sections.39.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.209.createdAt","sections.39.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.209.updatedAt","sections.39.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.210.createdAt","sections.39.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.210.updatedAt","sections.39.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.211.createdAt","sections.39.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.211.updatedAt","sections.40.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.212.createdAt","sections.40.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.212.updatedAt","sections.40.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.213.createdAt","sections.40.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.213.updatedAt","sections.40.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.214.createdAt","sections.40.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.214.updatedAt","sections.40.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.215.createdAt","sections.40.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.215.updatedAt","sections.41.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.216.createdAt","sections.41.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.216.updatedAt","sections.41.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.217.createdAt","sections.41.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.217.updatedAt","sections.41.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.218.createdAt","sections.41.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.218.updatedAt","sections.41.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.219.createdAt","sections.41.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.219.updatedAt","sections.41.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.220.createdAt","sections.41.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.220.updatedAt","sections.42.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.221.createdAt","sections.42.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.221.updatedAt","sections.42.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.222.createdAt","sections.42.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.222.updatedAt","sections.42.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.223.createdAt","sections.42.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.223.updatedAt","sections.42.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.224.createdAt","sections.42.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.224.updatedAt","sections.42.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.225.createdAt","sections.42.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.225.updatedAt","sections.42.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.226.createdAt","sections.42.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.226.updatedAt","sections.43.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.227.createdAt","sections.43.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.227.updatedAt","sections.43.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.228.createdAt","sections.43.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.228.updatedAt","sections.43.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.229.createdAt","sections.43.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.229.updatedAt","sections.43.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.230.createdAt","sections.43.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.230.updatedAt","sections.43.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.231.createdAt","sections.43.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.231.updatedAt","sections.43.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.232.createdAt","sections.43.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.232.updatedAt","sections.43.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.233.createdAt","sections.43.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.233.updatedAt","sections.43.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.234.createdAt","sections.43.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.234.updatedAt","sections.44.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.235.createdAt","sections.44.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.235.updatedAt","sections.44.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.236.createdAt","sections.44.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.236.updatedAt","sections.44.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.237.createdAt","sections.44.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.237.updatedAt","sections.44.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.238.createdAt","sections.44.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.238.updatedAt","sections.44.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.239.createdAt","sections.44.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.239.updatedAt","sections.44.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.240.createdAt","sections.44.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.240.updatedAt","sections.44.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.241.createdAt","sections.44.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.241.updatedAt","sections.44.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.242.createdAt","sections.44.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.242.updatedAt","sections.45.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.243.createdAt","sections.45.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.243.updatedAt","sections.45.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.244.createdAt","sections.45.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.244.updatedAt","sections.45.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.245.createdAt","sections.45.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.245.updatedAt","sections.45.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.246.createdAt","sections.45.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.246.updatedAt","sections.45.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.247.createdAt","sections.45.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.247.updatedAt","sections.45.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.248.createdAt","sections.45.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.248.updatedAt","sections.45.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.249.createdAt","sections.45.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.249.updatedAt","sections.46.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.250.createdAt","sections.46.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.250.updatedAt","sections.47.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.251.createdAt","sections.47.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.251.updatedAt","sections.47.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.252.createdAt","sections.47.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.252.updatedAt","sections.47.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.253.createdAt","sections.47.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.253.updatedAt","sections.47.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.254.createdAt","sections.47.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.254.updatedAt","sections.47.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.255.createdAt","sections.47.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.255.updatedAt","sections.48.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.256.createdAt","sections.48.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.256.updatedAt","sections.49.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.257.createdAt","sections.49.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.257.updatedAt","sections.49.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.258.createdAt","sections.49.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.258.updatedAt","sections.49.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.259.createdAt","sections.49.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.259.updatedAt","sections.49.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.260.createdAt","sections.49.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.260.updatedAt","sections.49.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.261.createdAt","sections.49.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.261.updatedAt","sections.50.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.262.createdAt","sections.50.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.262.updatedAt","sections.51.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.263.createdAt","sections.51.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.263.updatedAt","sections.51.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.264.createdAt","sections.51.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.264.updatedAt","sections.51.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.265.createdAt","sections.51.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.265.updatedAt","sections.51.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.266.createdAt","sections.51.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.266.updatedAt","sections.51.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.267.createdAt","sections.51.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.267.updatedAt","sections.52.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.268.createdAt","sections.52.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.268.updatedAt","sections.52.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.269.createdAt","sections.52.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.269.updatedAt","sections.52.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.270.createdAt","sections.52.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.270.updatedAt","sections.52.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.271.createdAt","sections.52.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.271.updatedAt","sections.52.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.272.createdAt","sections.52.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.272.updatedAt","sections.52.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.273.createdAt","sections.52.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.273.updatedAt","sections.52.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.274.createdAt","sections.52.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.274.updatedAt","sections.52.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.275.createdAt","sections.52.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.275.updatedAt","sections.53.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.276.createdAt","sections.53.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.276.updatedAt","sections.53.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.277.createdAt","sections.53.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.277.updatedAt","sections.53.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.278.createdAt","sections.53.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.278.updatedAt","sections.53.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.279.createdAt","sections.53.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.279.updatedAt","sections.53.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.280.createdAt","sections.53.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.280.updatedAt","sections.53.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.281.createdAt","sections.53.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.281.updatedAt","sections.54.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.282.createdAt","sections.54.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.282.updatedAt","sections.54.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.283.createdAt","sections.54.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.283.updatedAt","sections.54.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.284.createdAt","sections.54.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.284.updatedAt","sections.54.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.285.createdAt","sections.54.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.285.updatedAt","sections.54.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.286.createdAt","sections.54.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.286.updatedAt","sections.54.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.287.createdAt","sections.54.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.287.updatedAt","sections.55.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.288.createdAt","sections.55.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.288.updatedAt","sections.55.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.289.createdAt","sections.55.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.289.updatedAt","sections.55.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.290.createdAt","sections.55.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.290.updatedAt","sections.55.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.291.createdAt","sections.55.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.291.updatedAt","sections.55.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.292.createdAt","sections.55.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.292.updatedAt","sections.55.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.293.createdAt","sections.55.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.293.updatedAt","sections.55.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.294.createdAt","sections.55.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.294.updatedAt","sections.55.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.295.createdAt","sections.55.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.295.updatedAt","sections.56.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.296.createdAt","sections.56.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.296.updatedAt","sections.56.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.297.createdAt","sections.56.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.297.updatedAt","sections.56.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.298.createdAt","sections.56.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.298.updatedAt","sections.56.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.299.createdAt","sections.56.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.299.updatedAt","sections.56.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.300.createdAt","sections.56.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.300.updatedAt","sections.56.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.301.createdAt","sections.56.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.301.updatedAt","sections.57.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.302.createdAt","sections.57.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.302.updatedAt","sections.58.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.303.createdAt","sections.58.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.303.updatedAt","sections.58.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.304.createdAt","sections.58.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.304.updatedAt","sections.58.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.305.createdAt","sections.58.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.305.updatedAt","sections.58.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.306.createdAt","sections.58.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.306.updatedAt","sections.58.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.307.createdAt","sections.58.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.307.updatedAt","sections.59.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.308.createdAt","sections.59.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.308.updatedAt","sections.59.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.309.createdAt","sections.59.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.309.updatedAt","sections.59.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.310.createdAt","sections.59.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.310.updatedAt","sections.59.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.311.createdAt","sections.59.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.311.updatedAt","sections.59.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.312.createdAt","sections.59.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.312.updatedAt","sections.59.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.313.createdAt","sections.59.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.313.updatedAt","sections.60.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.314.createdAt","sections.60.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.314.updatedAt","sections.60.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.315.createdAt","sections.60.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.315.updatedAt","sections.60.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.316.createdAt","sections.60.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.316.updatedAt","sections.60.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.317.createdAt","sections.60.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.317.updatedAt","sections.60.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.318.createdAt","sections.60.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.318.updatedAt","sections.60.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.319.createdAt","sections.60.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.319.updatedAt","sections.60.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.320.createdAt","sections.60.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.320.updatedAt","sections.60.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.321.createdAt","sections.60.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.321.updatedAt","sections.60.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.322.createdAt","sections.60.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.322.updatedAt","sections.61.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.323.createdAt","sections.61.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.323.updatedAt","sections.61.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.324.createdAt","sections.61.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.324.updatedAt","sections.61.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.325.createdAt","sections.61.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.325.updatedAt","sections.61.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.326.createdAt","sections.61.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.326.updatedAt","sections.61.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.327.createdAt","sections.61.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.327.updatedAt"},"referentialEqualities":{"modules.0":["sections.0.modules.0"],"modules.1":["sections.0.modules.1"],"modules.2":["sections.0.modules.2"],"modules.3":["sections.0.modules.3"],"modules.4":["sections.1.modules.0"],"modules.5":["sections.1.modules.1"],"modules.6":["sections.1.modules.2"],"modules.7":["sections.2.modules.0"],"modules.8":["sections.2.modules.1"],"modules.9":["sections.2.modules.2"],"modules.10":["sections.2.modules.3"],"modules.11":["sections.2.modules.4"],"modules.12":["sections.2.modules.5"],"modules.13":["sections.2.modules.6"],"modules.14":["sections.2.modules.7"],"modules.15":["sections.3.modules.0"],"modules.16":["sections.3.modules.1"],"modules.17":["sections.3.modules.2"],"modules.18":["sections.3.modules.3"],"modules.19":["sections.3.modules.4"],"modules.20":["sections.4.modules.0"],"modules.21":["sections.4.modules.1"],"modules.22":["sections.4.modules.2"],"modules.23":["sections.4.modules.3"],"modules.24":["sections.4.modules.4"],"modules.25":["sections.4.modules.5"],"modules.26":["sections.4.modules.6"],"modules.27":["sections.4.modules.7"],"modules.28":["sections.4.modules.8"],"modules.29":["sections.5.modules.0"],"modules.30":["sections.5.modules.1"],"modules.31":["sections.5.modules.2"],"modules.32":["sections.5.modules.3"],"modules.33":["sections.5.modules.4"],"modules.34":["sections.5.modules.5"],"modules.35":["sections.5.modules.6"],"modules.36":["sections.5.modules.7"],"modules.37":["sections.6.modules.0"],"modules.38":["sections.6.modules.1"],"modules.39":["sections.6.modules.2"],"modules.40":["sections.6.modules.3"],"modules.41":["sections.6.modules.4"],"modules.42":["sections.6.modules.5"],"modules.43":["sections.7.modules.0"],"modules.44":["sections.7.modules.1"],"modules.45":["sections.8.modules.0"],"modules.46":["sections.8.modules.1"],"modules.47":["sections.8.modules.2"],"modules.48":["sections.8.modules.3"],"modules.49":["sections.8.modules.4"],"modules.50":["sections.8.modules.5"],"modules.51":["sections.8.modules.6"],"modules.52":["sections.9.modules.0"],"modules.53":["sections.9.modules.1"],"modules.54":["sections.9.modules.2"],"modules.55":["sections.9.modules.3"],"modules.56":["sections.9.modules.4"],"modules.57":["sections.9.modules.5"],"modules.58":["sections.10.modules.0"],"modules.59":["sections.10.modules.1"],"modules.60":["sections.11.modules.0"],"modules.61":["sections.11.modules.1"],"modules.62":["sections.11.modules.2"],"modules.63":["sections.11.modules.3"],"modules.64":["sections.11.modules.4"],"modules.65":["sections.12.modules.0"],"modules.66":["sections.12.modules.1"],"modules.67":["sections.12.modules.2"],"modules.68":["sections.12.modules.3"],"modules.69":["sections.12.modules.4"],"modules.70":["sections.12.modules.5"],"modules.71":["sections.13.modules.0"],"modules.72":["sections.14.modules.0"],"modules.73":["sections.14.modules.1"],"modules.74":["sections.14.modules.2"],"modules.75":["sections.14.modules.3"],"modules.76":["sections.14.modules.4"],"modules.77":["sections.14.modules.5"],"modules.78":["sections.14.modules.6"],"modules.79":["sections.14.modules.7"],"modules.80":["sections.15.modules.0"],"modules.81":["sections.15.modules.1"],"modules.82":["sections.15.modules.2"],"modules.83":["sections.15.modules.3"],"modules.84":["sections.15.modules.4"],"modules.85":["sections.15.modules.5"],"modules.86":["sections.15.modules.6"],"modules.87":["sections.16.modules.0"],"modules.88":["sections.16.modules.1"],"modules.89":["sections.16.modules.2"],"modules.90":["sections.16.modules.3"],"modules.91":["sections.16.modules.4"],"modules.92":["sections.16.modules.5"],"modules.93":["sections.17.modules.0"],"modules.94":["sections.17.modules.1"],"modules.95":["sections.18.modules.0"],"modules.96":["sections.18.modules.1"],"modules.97":["sections.18.modules.2"],"modules.98":["sections.18.modules.3"],"modules.99":["sections.19.modules.0"],"modules.100":["sections.19.modules.1"],"modules.101":["sections.19.modules.2"],"modules.102":["sections.19.modules.3"],"modules.103":["sections.19.modules.4"],"modules.104":["sections.19.modules.5"],"modules.105":["sections.19.modules.6"],"modules.106":["sections.19.modules.7"],"modules.107":["sections.20.modules.0"],"modules.108":["sections.20.modules.1"],"modules.109":["sections.20.modules.2"],"modules.110":["sections.21.modules.0"],"modules.111":["sections.21.modules.1"],"modules.112":["sections.21.modules.2"],"modules.113":["sections.22.modules.0"],"modules.114":["sections.22.modules.1"],"modules.115":["sections.22.modules.2"],"modules.116":["sections.22.modules.3"],"modules.117":["sections.22.modules.4"],"modules.118":["sections.22.modules.5"],"modules.119":["sections.23.modules.0"],"modules.120":["sections.23.modules.1"],"modules.121":["sections.23.modules.2"],"modules.122":["sections.23.modules.3"],"modules.123":["sections.23.modules.4"],"modules.124":["sections.23.modules.5"],"modules.125":["sections.24.modules.0"],"modules.126":["sections.24.modules.1"],"modules.127":["sections.24.modules.2"],"modules.128":["sections.24.modules.3"],"modules.129":["sections.24.modules.4"],"modules.130":["sections.24.modules.5"],"modules.131":["sections.24.modules.6"],"modules.132":["sections.25.modules.0"],"modules.133":["sections.25.modules.1"],"modules.134":["sections.25.modules.2"],"modules.135":["sections.26.modules.0"],"modules.136":["sections.26.modules.1"],"modules.137":["sections.26.modules.2"],"modules.138":["sections.26.modules.3"],"modules.139":["sections.27.modules.0"],"modules.140":["sections.27.modules.1"],"modules.141":["sections.27.modules.2"],"modules.142":["sections.27.modules.3"],"modules.143":["sections.27.modules.4"],"modules.144":["sections.27.modules.5"],"modules.145":["sections.28.modules.0"],"modules.146":["sections.28.modules.1"],"modules.147":["sections.28.modules.2"],"modules.148":["sections.28.modules.3"],"modules.149":["sections.28.modules.4"],"modules.150":["sections.28.modules.5"],"modules.151":["sections.28.modules.6"],"modules.152":["sections.28.modules.7"],"modules.153":["sections.29.modules.0"],"modules.154":["sections.29.modules.1"],"modules.155":["sections.29.modules.2"],"modules.156":["sections.30.modules.0"],"modules.157":["sections.30.modules.1"],"modules.158":["sections.30.modules.2"],"modules.159":["sections.31.modules.0"],"modules.160":["sections.31.modules.1"],"modules.161":["sections.31.modules.2"],"modules.162":["sections.31.modules.3"],"modules.163":["sections.31.modules.4"],"modules.164":["sections.31.modules.5"],"modules.165":["sections.31.modules.6"],"modules.166":["sections.31.modules.7"],"modules.167":["sections.32.modules.0"],"modules.168":["sections.32.modules.1"],"modules.169":["sections.32.modules.2"],"modules.170":["sections.32.modules.3"],"modules.171":["sections.32.modules.4"],"modules.172":["sections.32.modules.5"],"modules.173":["sections.33.modules.0"],"modules.174":["sections.33.modules.1"],"modules.175":["sections.33.modules.2"],"modules.176":["sections.34.modules.0"],"modules.177":["sections.34.modules.1"],"modules.178":["sections.34.modules.2"],"modules.179":["sections.35.modules.0"],"modules.180":["sections.35.modules.1"],"modules.181":["sections.35.modules.2"],"modules.182":["sections.35.modules.3"],"modules.183":["sections.35.modules.4"],"modules.184":["sections.35.modules.5"],"modules.185":["sections.35.modules.6"],"modules.186":["sections.36.modules.0"],"modules.187":["sections.36.modules.1"],"modules.188":["sections.36.modules.2"],"modules.189":["sections.36.modules.3"],"modules.190":["sections.36.modules.4"],"modules.191":["sections.36.modules.5"],"modules.192":["sections.37.modules.0"],"modules.193":["sections.37.modules.1"],"modules.194":["sections.37.modules.2"],"modules.195":["sections.37.modules.3"],"modules.196":["sections.37.modules.4"],"modules.197":["sections.37.modules.5"],"modules.198":["sections.38.modules.0"],"modules.199":["sections.38.modules.1"],"modules.200":["sections.38.modules.2"],"modules.201":["sections.38.modules.3"],"modules.202":["sections.38.modules.4"],"modules.203":["sections.38.modules.5"],"modules.204":["sections.39.modules.0"],"modules.205":["sections.39.modules.1"],"modules.206":["sections.39.modules.2"],"modules.207":["sections.39.modules.3"],"modules.208":["sections.39.modules.4"],"modules.209":["sections.39.modules.5"],"modules.210":["sections.39.modules.6"],"modules.211":["sections.39.modules.7"],"modules.212":["sections.40.modules.0"],"modules.213":["sections.40.modules.1"],"modules.214":["sections.40.modules.2"],"modules.215":["sections.40.modules.3"],"modules.216":["sections.41.modules.0"],"modules.217":["sections.41.modules.1"],"modules.218":["sections.41.modules.2"],"modules.219":["sections.41.modules.3"],"modules.220":["sections.41.modules.4"],"modules.221":["sections.42.modules.0"],"modules.222":["sections.42.modules.1"],"modules.223":["sections.42.modules.2"],"modules.224":["sections.42.modules.3"],"modules.225":["sections.42.modules.4"],"modules.226":["sections.42.modules.5"],"modules.227":["sections.43.modules.0"],"modules.228":["sections.43.modules.1"],"modules.229":["sections.43.modules.2"],"modules.230":["sections.43.modules.3"],"modules.231":["sections.43.modules.4"],"modules.232":["sections.43.modules.5"],"modules.233":["sections.43.modules.6"],"modules.234":["sections.43.modules.7"],"modules.235":["sections.44.modules.0"],"modules.236":["sections.44.modules.1"],"modules.237":["sections.44.modules.2"],"modules.238":["sections.44.modules.3"],"modules.239":["sections.44.modules.4"],"modules.240":["sections.44.modules.5"],"modules.241":["sections.44.modules.6"],"modules.242":["sections.44.modules.7"],"modules.243":["sections.45.modules.0"],"modules.244":["sections.45.modules.1"],"modules.245":["sections.45.modules.2"],"modules.246":["sections.45.modules.3"],"modules.247":["sections.45.modules.4"],"modules.248":["sections.45.modules.5"],"modules.249":["sections.45.modules.6"],"modules.250":["sections.46.modules.0"],"modules.251":["sections.47.modules.0"],"modules.252":["sections.47.modules.1"],"modules.253":["sections.47.modules.2"],"modules.254":["sections.47.modules.3"],"modules.255":["sections.47.modules.4"],"modules.256":["sections.48.modules.0"],"modules.257":["sections.49.modules.0"],"modules.258":["sections.49.modules.1"],"modules.259":["sections.49.modules.2"],"modules.260":["sections.49.modules.3"],"modules.261":["sections.49.modules.4"],"modules.262":["sections.50.modules.0"],"modules.263":["sections.51.modules.0"],"modules.264":["sections.51.modules.1"],"modules.265":["sections.51.modules.2"],"modules.266":["sections.51.modules.3"],"modules.267":["sections.51.modules.4"],"modules.268":["sections.52.modules.0"],"modules.269":["sections.52.modules.1"],"modules.270":["sections.52.modules.2"],"modules.271":["sections.52.modules.3"],"modules.272":["sections.52.modules.4"],"modules.273":["sections.52.modules.5"],"modules.274":["sections.52.modules.6"],"modules.275":["sections.52.modules.7"],"modules.276":["sections.53.modules.0"],"modules.277":["sections.53.modules.1"],"modules.278":["sections.53.modules.2"],"modules.279":["sections.53.modules.3"],"modules.280":["sections.53.modules.4"],"modules.281":["sections.53.modules.5"],"modules.282":["sections.54.modules.0"],"modules.283":["sections.54.modules.1"],"modules.284":["sections.54.modules.2"],"modules.285":["sections.54.modules.3"],"modules.286":["sections.54.modules.4"],"modules.287":["sections.54.modules.5"],"modules.288":["sections.55.modules.0"],"modules.289":["sections.55.modules.1"],"modules.290":["sections.55.modules.2"],"modules.291":["sections.55.modules.3"],"modules.292":["sections.55.modules.4"],"modules.293":["sections.55.modules.5"],"modules.294":["sections.55.modules.6"],"modules.295":["sections.55.modules.7"],"modules.296":["sections.56.modules.0"],"modules.297":["sections.56.modules.1"],"modules.298":["sections.56.modules.2"],"modules.299":["sections.56.modules.3"],"modules.300":["sections.56.modules.4"],"modules.301":["sections.56.modules.5"],"modules.302":["sections.57.modules.0"],"modules.303":["sections.58.modules.0"],"modules.304":["sections.58.modules.1"],"modules.305":["sections.58.modules.2"],"modules.306":["sections.58.modules.3"],"modules.307":["sections.58.modules.4"],"modules.308":["sections.59.modules.0"],"modules.309":["sections.59.modules.1"],"modules.310":["sections.59.modules.2"],"modules.311":["sections.59.modules.3"],"modules.312":["sections.59.modules.4"],"modules.313":["sections.59.modules.5"],"modules.314":["sections.60.modules.0"],"modules.315":["sections.60.modules.1"],"modules.316":["sections.60.modules.2"],"modules.317":["sections.60.modules.3"],"modules.318":["sections.60.modules.4"],"modules.319":["sections.60.modules.5"],"modules.320":["sections.60.modules.6"],"modules.321":["sections.60.modules.7"],"modules.322":["sections.60.modules.8"],"modules.323":["sections.61.modules.0"],"modules.324":["sections.61.modules.1"],"modules.325":["sections.61.modules.2"],"modules.326":["sections.61.modules.3"],"modules.327":["sections.61.modules.4"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792304068013,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningPaths","getBySlug"],{"input":{"slug":"machine-learning"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getBySlug\"],{\"input\":{\"slug\":\"machine-learning\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068673,"state":{"data":{"json":{"path":{"archivedAt":null,"id":"seed_b2b87dff658cc600bb5313282ab109da","slug":"machine-learning","title":"Machine Learning","description":"Learn the mathematical and engineering foundations of machine learning, explore executable visualizers, implement algorithms, and validate dataset releases in repository exercises.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Supervised Learning","Unsupervised Learning","Neural Networks","Deep Learning","Reinforcement Learning","ML Engineering"],"estimatedHours":252.3,"problemCount":39,"visualizerCount":12,"quizCount":0,"learningCount":277,"difficulty":"ADVANCED","idealFor":["Data scientists and ML engineers","Software engineers transitioning to ML","Researchers and academics","Anyone wanting to understand AI/ML deeply"],"prerequisites":["Basic programming (Python preferred)","High school math (algebra, basic calculus)","Comfort with mathematical notation"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithms","Deep learning architectures","Model training and evaluation","Feature engineering","ML system design"],"outcomes":["Build and train ML models from scratch","Understand the math behind algorithms","Apply appropriate algorithms to problems","Design neural network architectures","Deploy and maintain ML systems","Stay current with ML research"],"overview":"This comprehensive learning path takes you from mathematical foundations to cutting-edge ML techniques, preparing you for ML engineering and research roles.\n\n**What you'll learn:**\n• Mathematical foundations (linear algebra, calculus, probability)\n• Statistical learning theory\n• Regression and classification algorithms\n• Ensemble methods (Random Forests, XGBoost)\n• Model evaluation and hyperparameter tuning\n• Unsupervised learning (clustering, dimensionality reduction)\n• Bayesian methods and probabilistic models\n• Neural networks and deep learning\n• CNNs, RNNs, and Transformers\n• Generative models and reinforcement learning\n• ML engineering and deployment\n\nEach topic includes detailed explanations with mathematical rigor.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.124Z","modules":[{"id":"seed_458ccce80e8010395b21b166cb7157a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"What Is Machine Learning?","order":1,"contentType":"LEARNING","contentSlug":"what-is-machine-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a8d2537620b279c45bcc44b3c7e06647","title":"Formal Definition of Machine Learning","order":0},{"id":"seed_42ae00d692e83326eafe93c64c40bb17","title":"Learning from Data","order":1},{"id":"seed_a3a44c76b32adb410dc3fb4c6ffa340c","title":"Machine Learning vs Traditional Programming","order":2},{"id":"seed_1ea4f1f817746555c2ae12ee6c01297f","title":"Types of Learning: Supervised, Unsupervised, Reinforcement","order":3},{"id":"seed_779a6d64b4c2c855df560e5c96bea216","title":"Historical Perspective: The Evolution of Machine Learning","order":4}]},{"id":"seed_713656282288e703aa91193ff6640a1e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"The Machine Learning Pipeline","order":2,"contentType":"LEARNING","contentSlug":"the-ml-pipeline","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7b5489128cd6e7e05f3bed002cf61afb","title":"Problem Formulation","order":0},{"id":"seed_182a0a13f707287352f2006ac28a03a5","title":"Data Collection and Preparation","order":1},{"id":"seed_d345261d68ef95a3fd3a6c42a99479a0","title":"Feature Engineering","order":2},{"id":"seed_c41fad33c5bc70e85bf227cc118c53ac","title":"Model Selection and Training","order":3},{"id":"seed_a4db3b4deb24585d8eb0a84c1dbf9a1f","title":"Evaluation and Deployment","order":4}]},{"id":"seed_265702d858afc6a50a187b96538c09bf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Key Concepts and Terminology","order":3,"contentType":"LEARNING","contentSlug":"key-concepts-and-terminology","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cf917ae40b837629edc01d8626203d0b","title":"Features and Labels: The Language of Machine Learning Data","order":0},{"id":"seed_4b60b97fce71866f2f91c1bd6e21df4a","title":"Training, Validation, and Test Sets: The Foundation of Model Evaluation","order":1},{"id":"seed_1d2da31986bb79ef8b695328f0141d9a","title":"The Hypothesis Space: The Universe of Possible Models","order":2},{"id":"seed_34c88496a21b167b120c52098ade1ec9","title":"Loss Functions: Quantifying Model Error","order":3},{"id":"seed_cabd0a1a0688b3a963731496f4f1439b","title":"Generalization: The Ultimate Goal of Machine Learning","order":4}]},{"id":"seed_3f6d09c97b1e031f237b2abc340e3cbd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Machine Learning Success Factors","order":4,"contentType":"LEARNING","contentSlug":"ml-success-factors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_892fd230224ca5b877b22855e92c805a","title":"Data Quality and Quantity","order":0},{"id":"seed_e49b70b428629e50ef94ddf0aa1940b8","title":"Feature Representation","order":1},{"id":"seed_e1310a68c40d8c3fab7d75ca921d97e6","title":"Algorithm Selection","order":2},{"id":"seed_488bd94cf8d4015a6a3b18c3b8c6daca","title":"Computational Resources","order":3},{"id":"seed_abe193f4d7a82a578939e9a0ae77b0e5","title":"Domain Expertise","order":4}]},{"id":"seed_f1d78b0dba00cdd6d7891c54667b63c0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"What Is Machine Learning: The Foundation of Intelligent Systems","order":1,"contentType":"LEARNING","contentSlug":"what-is-ml","description":null,"estimatedMins":20,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_88cf405cd795a6a19169ce19aae9784d","title":"Formal Definition of Machine Learning","order":0},{"id":"seed_0b8431fdcbfce40e28ed571c25eb4faf","title":"Learning From Data","order":1},{"id":"seed_cadc325f21de5cb0d33f73a9130a21c6","title":"ML vs Traditional Programming","order":2},{"id":"seed_3136c4817ec4ac63d64b5ded9f84bbb8","title":"Types of Learning","order":3},{"id":"seed_a0add1ca687de3c16d9eb259e942a117","title":"Historical Perspective","order":4}]},{"id":"seed_3cd2b6f1422ffede96de1e0597239aa9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"The Machine Learning Landscape","order":2,"contentType":"LEARNING","contentSlug":"ml-landscape","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_5a7337716a8243ae6ac163168a9358a7","title":"Regression Problems","order":0},{"id":"seed_fab408ffe1d63967fa4033555fc3e147","title":"Classification Problems","order":1},{"id":"seed_1b70dfd5a9794b9e726f1932a75f534b","title":"Clustering Problems","order":2},{"id":"seed_68607512cc45e789ed2d9daf60162566","title":"Structured Prediction","order":3},{"id":"seed_bb20c6cdbd72b02b6b2ff2f86b708fbb","title":"Generative Modeling","order":4}]},{"id":"seed_be70ea380f07d6ea7e2bdcfc3143d40a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"When to Use Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"when-to-use-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a76436dc42aca300589c343e150664c7","title":"ML vs Rule-Based Systems","order":0},{"id":"seed_99a406a2e58c0bca81a9eb883bae835a","title":"Data Requirements for Machine Learning","order":1},{"id":"seed_1a1f40adc5952a5033af70a118210765","title":"Problem Complexity Assessment","order":2},{"id":"seed_1b0c603ce8398a59ea0709b89386c355","title":"Interpretability Needs","order":3},{"id":"seed_45439963ca15ec8736e63486ff7a18e4","title":"Cost-Benefit Analysis for ML Projects","order":4}]},{"id":"seed_0160e8621c11b759ae3cc62ac74840f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b249a75d4e7cf423557152dffdc02360","title":"Vectors as Ordered Lists","order":0},{"id":"seed_da63346642aa9a8e87f4b1393eb6091c","title":"Vector Operations","order":1},{"id":"seed_fe168a730e8cd78653fea1585f6b698f","title":"Linear Combinations","order":2},{"id":"seed_cd38d02c2681259630844c7a642def7b","title":"Span and Linear Independence","order":3},{"id":"seed_f4826e48bdca2a36fbaaaeabfffa900d","title":"Vector Spaces and Subspaces","order":4}]},{"id":"seed_62c3a9c331653ca339db3e3f28ff8baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Eigenvalues and Eigenvectors","order":2,"contentType":"LEARNING","contentSlug":"eigenvalues-and-eigenvectors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9f803806fff64c49ce44cc34930d91a8","title":"Eigenvalue Problem Definition","order":0},{"id":"seed_e431672dc58f8e16c08fa71e67f0c57b","title":"Geometric Interpretation","order":1},{"id":"seed_273883cd454631f7960194d9c6d25483","title":"Computing Eigenvalues","order":2},{"id":"seed_da8891e32b2170ca8b04aab194d1c4c2","title":"Diagonalization","order":3},{"id":"seed_b2ebf8e0471b83bd5e125f0d5854eff9","title":"Applications in ML (PCA, PageRank)","order":4}]},{"id":"seed_79a3d1325d227a51f27b9ef5234cd9a9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Matrix Decompositions","order":3,"contentType":"LEARNING","contentSlug":"matrix-decompositions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4f98567a3dc4b270d751df8319ebfd44","title":"Singular Value Decomposition (SVD)","order":0},{"id":"seed_e1bbecc3b6b08c0f06fa0ea425defc1d","title":"Eigendecomposition","order":1},{"id":"seed_f67c18eed67ce194cd29e83762ded3ea","title":"QR Decomposition","order":2},{"id":"seed_8e7d4c7b10ee5a045b407352347e3427","title":"Cholesky Decomposition","order":3},{"id":"seed_de9d56fa647c98ac1ee92c884fbf2ddd","title":"Applications in Machine Learning","order":4}]},{"id":"seed_1a7e06e15bba9acf14df3d33c9f77125","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Projections and Least Squares","order":4,"contentType":"LEARNING","contentSlug":"projections-and-least-squares","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2086b527c96bf555f32000d2e1736b82","title":"Orthogonal Projection","order":0},{"id":"seed_2104be958fbffc03f924f6fa378a2363","title":"Projection Matrices","order":1},{"id":"seed_ed1e375898cef23ccfb7f4b445b47955","title":"Normal Equations","order":2},{"id":"seed_8b41e076eab71545daec0e9b603c64b5","title":"Geometric View of Least Squares","order":3},{"id":"seed_e83e52dbb1beae0a6e5beed6d2f75a16","title":"Pseudoinverse","order":4}]},{"id":"seed_fe00df3a3ce80f8b8080b9d940134165","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":5,"contentType":"VISUALIZER","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_95423ea3af30d0a2a29f5e934888f366","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":6,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_58253712c8015db94782d197ddc36627","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Pairwise Matrix Product Computation","order":7,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_22c7972151c0c5ecdeb281eb35ce19d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":8,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_83ea72a6e731d4dbcd355861ca589959","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Matrices and Linear Transformations","order":1,"contentType":"LEARNING","contentSlug":"matrices-and-linear-transformations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4cd103eb060cc85b6f01d1e475bb1ab2","title":"Matrices as Linear Transformations","order":0},{"id":"seed_1adf7f5643518f2b456f4ab347e22784","title":"Matrix Operations","order":1},{"id":"seed_45029d7b2fca7b035cdc9d0f7186e0c0","title":"Matrix Multiplication Interpretation","order":2},{"id":"seed_5eba53ce1cf3188d0715abc741f997ac","title":"Rank and Nullity","order":3},{"id":"seed_296937a51259ce1a485f70f00a71586a","title":"Inverse Matrices","order":4}]},{"id":"seed_e52e478149e70c47f5ecdb61ff144516","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Norms and Distance Metrics","order":2,"contentType":"LEARNING","contentSlug":"norms-and-distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d4b9bc83f81877a3029c2ddc41bcee52","title":"Vector Norms (L1, L2, L∞)","order":0},{"id":"seed_237deae88d3c596844182f012a8a8121","title":"Matrix Norms","order":1},{"id":"seed_5b5f62c9ec9f8169d10958fd360b2cb7","title":"Distance Metrics","order":2},{"id":"seed_d7633facee2df16feee862ce5b47f614","title":"Similarity Measures","order":3},{"id":"seed_499ef81eebb4d9d03fe2a927e8f955cd","title":"Norm Regularization","order":4}]},{"id":"seed_1b167b3e9032b1f5cd4490b4f4ef40b2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":3,"contentType":"VISUALIZER","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_e4f70753ddb0e7daa22b01cddae4d0cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":4,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_d267450d674287a1391f487fe97633fa","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Feature Covariance Computation","order":5,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_715f767142a8ca5f68ecbe95188b9171","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_c9e167b399bfe6e2a2cd05b38ea7a6a0","title":"Derivatives and Partial Derivatives","order":0},{"id":"seed_ee450489f30583b22470ac414f0db899","title":"The Chain Rule","order":1},{"id":"seed_407f9675b96cb53355b75e1049d8e1b1","title":"Gradients","order":2},{"id":"seed_81a8ec988aa20438259f708dc5a6e268","title":"Directional Derivatives","order":3},{"id":"seed_2cb1c4dc90c61ba0d261e59125fca51c","title":"The Hessian Matrix","order":4}]},{"id":"seed_136b74dda11c6ede585436811675d040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Multivariate Calculus","order":2,"contentType":"LEARNING","contentSlug":"multivariate-calculus","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_ce27e0d0737553aa3dd2a5fec27a5b10","title":"Multivariable Functions — Foundations and Representations","order":0},{"id":"seed_809a050cbe29c7a691ddbdd9d856b24d","title":"Gradient Vectors — The Direction of Steepest Ascent","order":1},{"id":"seed_8b37c4d7357d63a1542f6e3efb093050","title":"Jacobian Matrices — Generalizing Derivatives to Vector Functions","order":2},{"id":"seed_3f36abe3c854e4be0ebf1f523d56d622","title":"Taylor Series Expansion — Approximating Functions Locally","order":3},{"id":"seed_b255469361018065decdb144e18ca48f","title":"Second-Order Approximations and the Hessian Matrix","order":4}]},{"id":"seed_5d93b2deb9a5171452b8f10b0927266b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Convex Optimization","order":3,"contentType":"LEARNING","contentSlug":"convex-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a62534e72db72ef29bb3b59299dd0d6e","title":"Convex Sets and Functions","order":0},{"id":"seed_87be7e753b48c155171285b56d265c0d","title":"Local vs Global Optima","order":1},{"id":"seed_5af7ee4b4f9e80296cdd65e3f42b5576","title":"First-Order Conditions","order":2},{"id":"seed_625c430c888f1228c222fc4b857a2e3f","title":"Second-Order Conditions","order":3},{"id":"seed_0b290cf8770e21f059694963f68426c5","title":"Convexity in ML","order":4}]},{"id":"seed_c17e63b9a5e2e3517a5c1a05e1c8687e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Descent","order":4,"contentType":"LEARNING","contentSlug":"gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_c1b8e66156a49e7a736fc4c98ffcfba3","title":"Gradient Descent Algorithm","order":0},{"id":"seed_3c124686ceeae53f663f43f9a98d9ed5","title":"Learning Rate Selection","order":1},{"id":"seed_943b4a2b8c7cbee179caa6129f61c060","title":"Convergence Analysis","order":2},{"id":"seed_2fa7c3ee753285bf9b4985d3d0abd094","title":"Variants: Batch, Stochastic, Mini-batch","order":3},{"id":"seed_e99fb1d5dfbc5d063b5fad188dacb836","title":"Momentum and Acceleration","order":4}]},{"id":"seed_58a6b4d26b778b4a73205b09fd21b0f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Constrained Optimization","order":5,"contentType":"LEARNING","contentSlug":"constrained-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_6eced085b9f76f74c478283576931600","title":"Lagrange Multipliers","order":0},{"id":"seed_622661cafc93e1cf7e1eee30e406d88f","title":"Karush-Kuhn-Tucker (KKT) Conditions","order":1},{"id":"seed_067090088a74e55e731d3f7c46d73b0d","title":"Duality: The Mirror World of Optimization","order":2},{"id":"seed_dd388937ab241c77e6fd44842642b230","title":"Dual Problem Formulation","order":3},{"id":"seed_cfd432f9c54180224edd043cb827233d","title":"Applications: Support Vector Machines","order":4}]},{"id":"seed_b0bf23bffa2221135e1bfa0877fc16a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Advanced Optimization Methods","order":6,"contentType":"LEARNING","contentSlug":"advanced-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_589a6b98aff004e39ca82a7c1e99bdb6","title":"Newton's Method","order":0},{"id":"seed_096687b06e5533b1372eeb8ae5616c71","title":"Quasi-Newton Methods (BFGS, L-BFGS)","order":1},{"id":"seed_d959397675821e4e63a0cdd5679cef77","title":"Coordinate Descent","order":2},{"id":"seed_c01022a29f9e92d7e5134127d547526b","title":"Proximal Methods","order":3},{"id":"seed_cc611f28262e9e8cb78954194121d39b","title":"Optimization Landscapes","order":4}]},{"id":"seed_87fed16558b28ac02f40835f8205b2eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":7,"contentType":"VISUALIZER","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_1e177e352ea702e7bdeb96bc180c96b0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":8,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_6538cb1ab6830bbd13c96c2518a5bbab","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Vector Computation for Scalar Fields","order":9,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_73aa88219e07d9fd5f5fb52f60c5bde7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d56be98f9a7734451103c90f4f54b9ae","title":"Sample Spaces and Events","order":0},{"id":"seed_4fe8e454d7b60a6389ac49408310cbe7","title":"Axioms of Probability","order":1},{"id":"seed_750aba734dba935aaf9b8606f4b4c2fe","title":"Conditional Probability","order":2},{"id":"seed_0dd8f1b09d7ee81a14fb89122f810455","title":"Independence","order":3},{"id":"seed_954fbbb4fefae33dc24611cf53b5d0a7","title":"Bayes' Theorem","order":4}]},{"id":"seed_501a2bc6ca2690e1bb6027ebab947dbb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Random Variables and Distributions","order":2,"contentType":"LEARNING","contentSlug":"random-variables-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9a96e400f4a0b79e93e447aabee3ac1f","title":"Discrete Random Variables","order":0},{"id":"seed_d4c85b11ec12710bd64bab83c6d97fc1","title":"Continuous Random Variables","order":1},{"id":"seed_db0d600ac28f0bb1b92f18e3ac6d0d25","title":"PMF, PDF, and CDF","order":2},{"id":"seed_fabbdf76198597f92fc3292499152dbe","title":"Expectation and Variance","order":3},{"id":"seed_758b4d58f78986a9ab02372bec41ad41","title":"Moments","order":4}]},{"id":"seed_4bedc7e467feee3964d3f6d9adc5c5a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Common Probability Distributions","order":3,"contentType":"LEARNING","contentSlug":"common-probability-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_0c9a85329763186db1f715343058c80f","title":"Bernoulli and Binomial Distributions","order":0},{"id":"seed_25f719c3273145b70eb2f19c69355475","title":"Gaussian (Normal) Distribution","order":1},{"id":"seed_115fcdefdc6e70187668e21bff7b7484","title":"Poisson Distribution","order":2},{"id":"seed_924e0c15a9b88f21fdd72e29b0d09cc8","title":"Exponential Family of Distributions","order":3},{"id":"seed_aa0b8ec7d40e7b9c2d723080a411f1c6","title":"Multivariate Gaussian Distribution","order":4}]},{"id":"seed_de316035d6a3b7c5c82a9b8d273802de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Joint and Marginal Distributions","order":4,"contentType":"LEARNING","contentSlug":"joint-marginal-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4c687dc6d5f4dd83f7f8ffdd04fb0404","title":"Joint Distributions","order":0},{"id":"seed_2202654b56bf7ce2e1e6358b9b605b80","title":"Marginal Distributions","order":1},{"id":"seed_0221448e7bba3022344ddde383da1de6","title":"Conditional Distributions","order":2},{"id":"seed_d8d5ae5b8ec072dfee02dfb89c2dc130","title":"Covariance and Correlation","order":3},{"id":"seed_24f5f9a8609c4acbddfede295342f551","title":"Independence","order":4}]},{"id":"seed_3e205a6ba092f97e52401620397afc15","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Estimation Theory","order":5,"contentType":"LEARNING","contentSlug":"estimation-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_e3a8b67c5ec75fdffbc77263b10fd3d3","title":"Maximum Likelihood Estimation (MLE)","order":0},{"id":"seed_9d5719ab49afa4ca49341ef7d1bce70b","title":"Maximum A Posteriori (MAP) Estimation","order":1},{"id":"seed_5a2922e603c249fec1cec76f5a36ce9a","title":"Bias and Variance of Estimators","order":2},{"id":"seed_a6103baa2d58d0fc08937d947c6b8289","title":"Consistency of Estimators","order":3},{"id":"seed_af0f2c9c163f136cc39c378528e68501","title":"Efficiency of Estimators","order":4}]},{"id":"seed_9d79f7e518e418caedc3c16ad4567312","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Information Theory for Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"information-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_403e429e44b0906096d7cd03c2f49b0c","title":"Entropy: Quantifying Uncertainty","order":0},{"id":"seed_7299687f9c6cd446106fa16599a6204c","title":"Cross-Entropy: The Language of Classification Loss","order":1},{"id":"seed_f4ca0321720d0706e26a95c980f6bfcd","title":"KL Divergence: Measuring Distributional Difference","order":2},{"id":"seed_205f1efa0dd32e9fd058df697858811d","title":"Mutual Information: Quantifying Shared Information","order":3},{"id":"seed_d7f47bfc8afd583915bff0be3fbd484d","title":"Information Theory in Machine Learning: A Unified View","order":4}]},{"id":"seed_c5e69fdba5ec3502e7ecb9a2729a06be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Population Mean Confidence Estimator","order":7,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_5b652c4511070dff688b90df174e04cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Independent Samples Mean Comparison","order":8,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_66416329d1070e25819caacd6051f101","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Learning Problem","order":1,"contentType":"LEARNING","contentSlug":"the-learning-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_efd227ef78ab77f88417bcaec01baf26","title":"Formal Learning Framework","order":0},{"id":"seed_e527b2a901d29dadb1057f4f33e118f9","title":"Empirical Risk Minimization","order":1},{"id":"seed_d1100ad43324f51b8abc85f7baa5536a","title":"True Risk vs Empirical Risk","order":2},{"id":"seed_739bc97e30205aa6637cf74635ace69e","title":"Generalization Gap","order":3},{"id":"seed_c10617fbc9750999220fb74d60d5f1f1","title":"No Free Lunch Theorem","order":4}]},{"id":"seed_27b627da90e780a7304764b0313cb5a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"PAC Learning: Probably Approximately Correct Framework","order":2,"contentType":"LEARNING","contentSlug":"pac-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_6442d1525ca0eef60c1763dada8b1a66","title":"Probably Approximately Correct Framework","order":0},{"id":"seed_6623868e6dc7b18f190e099db36a6a8c","title":"Sample Complexity","order":1},{"id":"seed_449f076162b461f08936b9cffc878fc7","title":"PAC-Learnability","order":2},{"id":"seed_0715644b93cd64d3f33704bb008d977d","title":"Finite Hypothesis Classes","order":3},{"id":"seed_d1c50019a25b196405bc95d1712a936e","title":"Computational Complexity","order":4}]},{"id":"seed_91308bda8e0cece0354a33420f6bab7c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"VC Dimension and Model Complexity","order":3,"contentType":"LEARNING","contentSlug":"vc-dimension","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_837b0197b58f58800b6929d82917562c","title":"Shattering: The Foundation of VC Theory","order":0},{"id":"seed_0322860aedd69fdb804fbe7aec5e68e3","title":"VC Dimension: Formal Definition and Properties","order":1},{"id":"seed_15d60f4805b3fc39c9218948a64ceccc","title":"VC Dimension of Linear Classifiers","order":2},{"id":"seed_47246e73be4769f88b5b1c581d4db307","title":"Generalization Bounds from VC Dimension","order":3},{"id":"seed_34b2d48d22ea18b27e951e037b0ba7d3","title":"Structural Risk Minimization","order":4}]},{"id":"seed_74a06a67e984c7f33ea72321e27fe638","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Bias-Variance Tradeoff","order":4,"contentType":"LEARNING","contentSlug":"bias-variance-tradeoff","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a1f220cf59d307c9033e2f5989d13063","title":"Bias-Variance Decomposition","order":0},{"id":"seed_dd1180784ccf308f3e00280f30397677","title":"Sources of Error","order":1},{"id":"seed_70bbcb80ed75408b2697ec7516b93221","title":"Model Complexity","order":2},{"id":"seed_e5ad0cc737f80687f8fd4169dafea187","title":"Tradeoff Visualization","order":3},{"id":"seed_3d9b7d2c2d31b7e5826d314f68722c42","title":"Implications for Model Selection","order":4}]},{"id":"seed_6f12c2ff794f5c0a3d28930627f9e9ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Regularization Theory","order":5,"contentType":"LEARNING","contentSlug":"regularization-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b81489822980346014c9e0db33aa22cf","title":"Regularization Motivation","order":0},{"id":"seed_9bc99362d1468932347d98eeafca8f6a","title":"Regularization as Constraint","order":1},{"id":"seed_08658b1c87830994c56a47693372da9d","title":"Regularization as Prior","order":2},{"id":"seed_156e099a21f52dbdc2bd305c8b4fde9c","title":"Effects on Generalization","order":3},{"id":"seed_44a18836eea49bfe7fdafd4c2e52caa8","title":"Common Regularizers","order":4}]},{"id":"seed_8848fbde5a5ec45b7dcbbb6bcd4ab9ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Generalization Bounds: Mathematical Guarantees for Learning","order":6,"contentType":"LEARNING","contentSlug":"generalization-bounds","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2b9e3d28d1c3cb4bac3e0a51fbc5a0f5","title":"The Union Bound: Foundation of Generalization Theory","order":0},{"id":"seed_5d674cff25592aa0c6073f4dee28e6cc","title":"Hoeffding's Inequality: Exponential Concentration for Bounded Variables","order":1},{"id":"seed_21e7c3e6ad7c0f28eb583ae7953ea97b","title":"Rademacher Complexity: Data-Dependent Generalization Bounds","order":2},{"id":"seed_cc23793c711008d6fbe01e8c5fd44633","title":"Margin-Based Bounds: Geometric Structure for Generalization","order":3},{"id":"seed_f07b20cc7fbcef6aaae51aee140f7112","title":"PAC-Bayes Bounds: Bayesian Generalization Theory","order":4}]},{"id":"seed_82fa8bc43938f7a884129594f86d7d81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Regression Diagnostics","order":1,"contentType":"LEARNING","contentSlug":"regression-diagnostics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2ec0a15230f2da55ced82fc8aad6d13a","title":"Residual Analysis","order":0},{"id":"seed_a96b5be71fe3dc6e59214ac688967b26","title":"Normality Tests","order":1},{"id":"seed_19b6a00db2e39664afebf2509ef30922","title":"Heteroscedasticity","order":2},{"id":"seed_4a4f57ebe5b39237814f4bbe50047178","title":"Influential Points","order":3},{"id":"seed_32e3e905cb78afc0042e4b9c4725c143","title":"Multicollinearity","order":4}]},{"id":"seed_c6e739d9eb19caa6b379150681b5e7cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Computational Considerations in Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"computational-considerations-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cc08b5f3b89ab17cc80cc1ae495a2ad1","title":"Numerical Stability","order":0},{"id":"seed_2e8a100634acba71f9d69d243b21c1ac","title":"QR Decomposition Solution","order":1},{"id":"seed_8f7a6e10bd46099a2b5e6949d6259d4d","title":"Complexity Analysis","order":2},{"id":"seed_d626d357937e0d9615a995e9fb9fb771","title":"Online Updates","order":3},{"id":"seed_01a54def315c67fdbf7d74a929a6037a","title":"Weighted Least Squares","order":4}]},{"id":"seed_829f2ade22abec4e2b345562ce75988e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_97d46af62464a1e775d8b099e3dce5c5","title":"Model Formulation","order":0},{"id":"seed_6632aadcd0a92a39a62c0ddaff84a755","title":"Least Squares Derivation","order":1},{"id":"seed_5302711156dc7225bf7df36434b35477","title":"Geometric Interpretation","order":2},{"id":"seed_b86b4d4971f612f160049abff5b34766","title":"Coefficient Interpretation","order":3},{"id":"seed_f2af5d8fd60deee5e1e01602b4b096b4","title":"Assumptions","order":4}]},{"id":"seed_f2efa2b99627070158e4ef4b79c540a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Multiple Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"multiple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4447d3d0f9a46eb6fd265ea635835a29","title":"Matrix Formulation","order":0},{"id":"seed_198093b140733c0df22020bf40c907c5","title":"Normal Equations Derivation","order":1},{"id":"seed_d5f53539876c2787304eb4a7338f11a8","title":"OLS Estimator Properties","order":2},{"id":"seed_0ffbfe958196b00c5f6eee50c4f54c17","title":"Geometric Interpretation","order":3},{"id":"seed_84ca7315399fdc1f7552ce87f6386959","title":"Projection Perspective","order":4}]},{"id":"seed_a7bdfcdeda9f1afd7fb7b9fc202d3f81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Statistical Properties of OLS","order":3,"contentType":"LEARNING","contentSlug":"statistical-properties-of-ols","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4cf8c398ca23d6bffdfb16838b4c746f","title":"The Gauss-Markov Theorem","order":0},{"id":"seed_0bdee0d7b2b47780c5a6645c25cf6a5d","title":"The BLUE Property and Minimum Variance Estimation","order":1},{"id":"seed_e9cebcb4c43060e8ed5692d74af7bb6c","title":"Variance of OLS Estimates and Standard Errors","order":2},{"id":"seed_67721dbeff81ffbb90ae6357969397e6","title":"Confidence Intervals for Regression Coefficients","order":3},{"id":"seed_28498b0501da539124ab8301abc4ad70","title":"Hypothesis Testing for Regression Coefficients","order":4}]},{"id":"seed_916ec3501e133742dec47a2102539082","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Maximum Likelihood View of Linear Regression","order":4,"contentType":"LEARNING","contentSlug":"maximum-likelihood-view","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_32f27f1b01674466e994bd1e21527370","title":"Gaussian Noise Assumption","order":0},{"id":"seed_8db83899bb21c42622e269e570227e99","title":"Log-Likelihood Derivation","order":1},{"id":"seed_fb879277abb35c208e0cc42a1467e3b7","title":"MLE Solution","order":2},{"id":"seed_061e6610cb74f4ed7f33b7c0590f1d55","title":"Connection to OLS","order":3},{"id":"seed_45512c47296921d0fe3bddb11626af61","title":"Variance Estimation","order":4}]},{"id":"seed_37c09e6af50bedd36be1dc9600378f95","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":5,"contentType":"VISUALIZER","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_1eaf702ff974ccd2ce05bd4f9821f236","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":6,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_ff8d00adbbea598d4881affb39e91b05","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":7,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_d97a7c01ffd0b34a093375e7578d029d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"The Overfitting Problem","order":1,"contentType":"LEARNING","contentSlug":"the-overfitting-problem","description":null,"estimatedMins":40,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_e876e40a3595abeca4651398e0ee17b0","title":"Training vs Test Error","order":0},{"id":"seed_3882ab2d79f353ce2117df7a2b816e1c","title":"High-Dimensional Settings","order":1},{"id":"seed_751af5c393757310899d3b58eedfa5f8","title":"Variance Explosion","order":2},{"id":"seed_17c0aa662cf495807c0daa75fd463f27","title":"Need for Regularization","order":3}]},{"id":"seed_12992e935695a3adf10d331bc32c3762","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Ridge Regression (L2 Regularization)","order":2,"contentType":"LEARNING","contentSlug":"ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b9e9dfe0ae36df514073f9cceecb946b","title":"L2 Penalty Formulation","order":0},{"id":"seed_4758f319a81fc4635807084ab562b006","title":"Closed-Form Solution","order":1},{"id":"seed_b893e733a06e377d936fc13e0ad1b589","title":"Shrinkage Interpretation","order":2},{"id":"seed_f344d9adfe936a8ea7995abfb69ccc75","title":"Bias-Variance Tradeoff","order":3},{"id":"seed_c8252a8fbb7a984d1704e5e753096f16","title":"Selecting Regularization Strength","order":4}]},{"id":"seed_6bb2f7c3adb3a36845ac25fd5e6983ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Lasso Regression (L1 Regularization)","order":3,"contentType":"LEARNING","contentSlug":"lasso-regression-l1","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_3c09683231831d59fa0c1341cf71bd7c","title":"L1 Penalty Formulation","order":0},{"id":"seed_818ba9c95903630e6a47446b7791e730","title":"Sparsity Inducing Property","order":1},{"id":"seed_44a5a072c981a0ae68784263e944f5a5","title":"Geometric Interpretation","order":2},{"id":"seed_05224b981d79da19989db9da16935549","title":"Solution Algorithms (Coordinate Descent)","order":3},{"id":"seed_c73fe0f7506794f407d6467452dbc493","title":"Feature Selection Aspect","order":4}]},{"id":"seed_467edb60b91d6f7d7c4cb891f946f6ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Elastic Net Regularization","order":4,"contentType":"LEARNING","contentSlug":"elastic-net","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d81e4bbcf1af089a0e00779ec50c7594","title":"Combined L1/L2 Penalty - The Elastic Net Formulation","order":0},{"id":"seed_6d4c16f2b29898e4285d84eca2cbd8e5","title":"The Grouping Effect - Handling Correlated Features","order":1},{"id":"seed_aed479a1d15635b50b697103575fcc0d","title":"When to Use Elastic Net - Decision Framework","order":2},{"id":"seed_efc7146ca3a153d35cccd455da439d17","title":"Hyperparameter Selection - Tuning α and λ","order":3},{"id":"seed_e524ea838d1dc58a7958afe4b2c3f77c","title":"Practical Considerations - Implementation and Best Practices","order":4}]},{"id":"seed_01a0334a7189eee091e1369be1d858ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Bayesian Interpretation of Regularization","order":5,"contentType":"LEARNING","contentSlug":"bayesian-interpretation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_96a3322179113f456995f9d94bb886aa","title":"Prior Distributions","order":0},{"id":"seed_e7ba2c6b0d56c8a37695196c01101cd9","title":"Ridge as Gaussian Prior","order":1},{"id":"seed_33e61455247cfc0103a3b52c41eb9ea5","title":"Lasso as Laplace Prior","order":2},{"id":"seed_cf5153235af37459b83742855ad8afdf","title":"MAP Estimation","order":3},{"id":"seed_65f4100e8b885642ab9738682a137827","title":"Posterior Inference","order":4}]},{"id":"seed_539e0c0df2b9d3fc72bfe1ebe1830538","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Advanced Regularization Techniques","order":6,"contentType":"LEARNING","contentSlug":"advanced-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_0e1a8424672fa6d6854c7e4bbf0a35cf","title":"Group Lasso","order":0},{"id":"seed_32c6d5d2bd344c6dab4670ae261048b4","title":"Fused Lasso","order":1},{"id":"seed_18547794abe33afafaa35e72befe0479","title":"Bridge Regression","order":2},{"id":"seed_27b95f0db67e6780f5f9daeeca6d45a9","title":"Non-convex Penalties","order":3},{"id":"seed_5a7234f1fdeedf4868b4a74cad826ad5","title":"Structured Regularization","order":4}]},{"id":"seed_598afbdc7bc7fa0451b024fbbb08b0a1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Kernel Ridge Regression","order":1,"contentType":"LEARNING","contentSlug":"kernel-ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_61d29cce4d0a2f0baf953d82931d2c0c","title":"Dual Formulation","order":0},{"id":"seed_942cdb30b511c6f602fa830ae9156036","title":"Kernel Matrix","order":1},{"id":"seed_d4d222769bf438256499bc494c8ee3de","title":"Prediction with Kernels","order":2},{"id":"seed_eaf5187dd53528848180a4f65853ee22","title":"Computational Complexity","order":3},{"id":"seed_94bd807fa06f2793f86be328bf1d845d","title":"Kernel Selection","order":4}]},{"id":"seed_723eff8f5cc227d1626cccbd013817f2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Gaussian Processes Introduction","order":2,"contentType":"LEARNING","contentSlug":"gaussian-processes-introduction","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_1ff629386ecbd9ea503e727498e67afa","title":"GP as Distribution Over Functions","order":0},{"id":"seed_f065df7d5252178409e62f1671694ca4","title":"Prior Specification","order":1},{"id":"seed_30ebf4265054c973e021ae605f4c4bc4","title":"Posterior Inference","order":2},{"id":"seed_439bcae38aaaeff820f5d0eea97e8287","title":"Mean and Variance Prediction","order":3},{"id":"seed_7723bfbcf95297e50b31ebe3f5e84825","title":"GP vs Kernel Ridge","order":4}]},{"id":"seed_cab2603b0eff0b9065ccf19343b8f194","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"The Kernel Trick","order":1,"contentType":"LEARNING","contentSlug":"the-kernel-trick","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_1fa76caff1bb51ac49acd9308b6ab4e5","title":"Feature Space Mapping","order":0},{"id":"seed_2c41b9b3cc0701f340fa3941e197cc62","title":"Kernel Functions","order":1},{"id":"seed_80125b02ec588f7386dc54e2c9ad3be5","title":"Computational Advantage","order":2},{"id":"seed_cbc0699f5b8262ba515bc1430d3b7df6","title":"Mercer's Theorem","order":3},{"id":"seed_732643bdf91fe1019c973c38bea5c472","title":"Common Kernels","order":4}]},{"id":"seed_bc8bcb824c6863c43427f9eee6e7f874","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Gaussian Process Kernels and Hyperparameter Optimization","order":2,"contentType":"LEARNING","contentSlug":"gp-kernels-and-hyperparameters","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b0606699677faeb2d1000e3442f0cb42","title":"Kernel Design: The Foundation of Gaussian Process Modeling","order":0},{"id":"seed_1c6091d51d8679c94308ad1738a86c3e","title":"Combining Kernels: Building Expressive Models","order":1},{"id":"seed_310979f943ba7d1e0080b946d68ef242","title":"Hyperparameter Optimization for Gaussian Processes","order":2},{"id":"seed_9e07db9cb43257ca92203b9665d9a174","title":"The Marginal Likelihood: Bayesian Model Selection","order":3},{"id":"seed_def1724cb40f5ad55866b232b04932fa","title":"Automatic Relevance Determination (ARD)","order":4}]},{"id":"seed_a8ccd12986493122718a2e8f610b8745","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Computational Aspects of Kernels","order":3,"contentType":"LEARNING","contentSlug":"computational-aspects-of-kernels","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7dbfa55ec53db8fc1c47c82e317e041e","title":"Kernel Matrix Complexity","order":0},{"id":"seed_51fe86cb860554294efeb648105e1b20","title":"Random Fourier Features","order":1},{"id":"seed_5add71fc85649f27507938d1a1ee8fdf","title":"Nyström Approximation","order":2},{"id":"seed_ed5ce57d6b6d7df6358974a55e4cd670","title":"Sparse Gaussian Processes","order":3},{"id":"seed_7ce4cec95f2d488acb433272c2232b8f","title":"Scalability Strategies","order":4}]},{"id":"seed_6242715024a6f8cd75cba825c4cf2e87","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel Selection and Design","order":4,"contentType":"LEARNING","contentSlug":"kernel-selection-and-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9da5aed85bf1b36b85d7ff4287409925","title":"Kernel Families","order":0},{"id":"seed_dd691249e582ead6397a895f1932f384","title":"Domain-Specific Kernels","order":1},{"id":"seed_02cbd46742318300210d5a9785ac30fd","title":"Multiple Kernel Learning","order":2},{"id":"seed_967b6c680f5221491c7cf113462b2635","title":"Kernel Alignment","order":3},{"id":"seed_b496a55e6f91ac91782fd21262c67176","title":"Universal Kernels","order":4}]},{"id":"seed_766bbb1d8379c0966fce86e377d8aa45","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel-Based Support Vector Classifier Training","order":5,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_480cc6f8d716ebf111dd33d18fc8ce41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Polynomial Regression","order":1,"contentType":"LEARNING","contentSlug":"polynomial-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_5b12d1b3ac2c838155a1e47b728aa861","title":"Polynomial Features","order":0},{"id":"seed_40819aeac8f0eb99b896ef814b5e3924","title":"Degree Selection","order":1},{"id":"seed_2e45846fcd2d89e7c8309d4ac0a13946","title":"Overfitting Considerations","order":2},{"id":"seed_5982150413e065d9f3d8b7356ab888c9","title":"Orthogonal Polynomials","order":3},{"id":"seed_9bebfbc640e27943fcaa44c704ccb19f","title":"Piecewise Polynomials","order":4}]},{"id":"seed_6f3094d5cfb0e003129f88a934111408","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Spline Regression Methods","order":2,"contentType":"LEARNING","contentSlug":"spline-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_baf710dedf209853358c5a0b82fd849c","title":"Basis Splines (B-splines): The Foundation of Modern Spline Regression","order":0},{"id":"seed_c77bcf9dc30bf73ac93dad1753e6c11f","title":"Natural Splines: Optimal Boundary Behavior","order":1},{"id":"seed_9fb1e34712ba8dd5f1de915db3ca1d2c","title":"Smoothing Splines: The Optimization Framework","order":2},{"id":"seed_b26995ab647e7d9151911fb299645954","title":"Knot Selection: Strategies for Optimal Spline Flexibility","order":3},{"id":"seed_bd1a97174963a61038945c7aba819739","title":"Penalized Splines (P-splines): The Best of Both Worlds","order":4}]},{"id":"seed_fd56c51bbb955a03cd319577e559ca24","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Generalized Additive Models (GAMs)","order":3,"contentType":"LEARNING","contentSlug":"generalized-additive-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_91990fc3c684d915deda29466a200e0e","title":"Additive Structure","order":0},{"id":"seed_549dc5ac684ad125fe7a2a12bfddd9a9","title":"Component Functions","order":1},{"id":"seed_f1ae5bcded7464e5d28a5a5a2c1542e9","title":"Fitting Algorithms","order":2},{"id":"seed_32bcc722e4aa5bfef4732b81616b432e","title":"Interpretation","order":3},{"id":"seed_765b57d00b1ccdda5c262f086c407d7c","title":"Extensions","order":4}]},{"id":"seed_23ca6d0844f4fd9cf359b5e42366acae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Quantile Regression","order":4,"contentType":"LEARNING","contentSlug":"quantile-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_da2fc39be52dc34f8899f788c1b5bec5","title":"Quantile Loss Function","order":0},{"id":"seed_e3bc0a8f2bad307de48dc50902351f21","title":"Pinball Loss Function","order":1},{"id":"seed_4735e93544ee065c810528ee57495ec6","title":"Conditional Quantiles","order":2},{"id":"seed_9d6d1d25d2812571991ff8c60dbbe70e","title":"Prediction Intervals","order":3},{"id":"seed_d19a239cab2566296ca9b5234d2f22e6","title":"Robust Estimation","order":4}]},{"id":"seed_20d9052919ee9f6e5926ffc684095840","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Robust Regression Methods","order":5,"contentType":"LEARNING","contentSlug":"robust-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7379bb4970fae91e9a0dc5a987eb96f7","title":"Huber Loss: The Bridge Between L1 and L2","order":0},{"id":"seed_bccba192df86c8260ffb78eebb4da3da","title":"M-Estimators: The General Framework for Robust Estimation","order":1},{"id":"seed_1224b7833296543b9e6706e5c2497c98","title":"RANSAC: Random Sample Consensus for Extreme Outlier Robustness","order":2},{"id":"seed_a25af89e3140585e2503e7d85c172ed9","title":"Breakdown Point: The Ultimate Measure of Robustness","order":3},{"id":"seed_91862f58123af0f29bdb3ab65a4ade05","title":"Influence Functions: Understanding Local Robustness","order":4}]},{"id":"seed_d7ab2a7eb1f69de228f455ae070e7934","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Nonparametric Regression","order":6,"contentType":"LEARNING","contentSlug":"nonparametric-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_3cbe6308ac967d286d0449235b83352c","title":"Local Regression (LOESS/LOWESS)","order":0},{"id":"seed_054e9a035b1f855cbf2ab3c6eafc4e69","title":"Kernel Smoothing","order":1},{"id":"seed_a9342bbc00f47e961da1ab3700b4fc4c","title":"Bandwidth Selection","order":2},{"id":"seed_b9511280fb2a78bb01a308bb3600ab69","title":"Curse of Dimensionality","order":3},{"id":"seed_7089e4852501b5670aec7555bbd660d1","title":"When to Use Nonparametric Methods","order":4}]},{"id":"seed_78a01a27b8d25b02cf4b06660cd62e0f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression And Glms","sectionOrder":14,"title":"Regularized Logistic Regression","order":1,"contentType":"LEARNING","contentSlug":"module-5-regularized-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_88856996fd54d279febcbbe73fe3ff59","title":"L2 Regularization in Logistic Regression","order":0},{"id":"seed_f6d21c094bc3670091af8771243d7978","title":"L1 Regularization (Lasso) in Logistic Regression","order":1},{"id":"seed_ce94093763a7eba307abbe6f2e1f0a18","title":"Elastic Net Regularization in Logistic Regression","order":2},{"id":"seed_f583526ee776713b8383d5e6db04be0f","title":"Regularization Path","order":3},{"id":"seed_342fae968acb37817b71044d0e60ad3b","title":"Hyperparameter Selection","order":4}]},{"id":"seed_fd8976631490d38a493a49d4ea06d921","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Binary Classification Problem","order":1,"contentType":"LEARNING","contentSlug":"binary-classification-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a4bcd986671cfa0e4fee85afc782ef2d","title":"Problem Formulation","order":0},{"id":"seed_f3f2afa2d1ac3b6aedca9e94a2b791bf","title":"Decision Boundaries","order":1},{"id":"seed_1657663f43e1973612cb12ee6ead2ff1","title":"Probabilistic Classification","order":2},{"id":"seed_888f539a93c61b5142fb32bb016e0e4e","title":"Linear Classifiers","order":3},{"id":"seed_c5bc31811892352ac9422f118f8e3ab9","title":"Loss Functions for Classification","order":4}]},{"id":"seed_a84bde3ef9a84b2740b90dbf47496bc4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"The Logistic Regression Model","order":2,"contentType":"LEARNING","contentSlug":"logistic-regression-model","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7fc1febbf114c98ca5eae8e78d777c6d","title":"The Sigmoid Function","order":0},{"id":"seed_b87855bbcb2de262175a8f9861fffe43","title":"Log-Odds Interpretation","order":1},{"id":"seed_3a766c5c6697ac9b680f2bc5d0d3363b","title":"Model Parameters","order":2},{"id":"seed_4cb3ee219d08d0512d24de65d04f5777","title":"Decision Boundary","order":3},{"id":"seed_bb6c1b50bf1eaadb048b624616fa52ba","title":"Probabilistic Interpretation","order":4}]},{"id":"seed_7c3373282cd644a5e90d3ce19e000cae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Maximum Likelihood Estimation for Logistic Regression","order":3,"contentType":"LEARNING","contentSlug":"mle-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_369a33ed7306d84533926316bceffc1c","title":"Likelihood Function","order":0},{"id":"seed_17d45d86be758347f1482ddbbbd2de7a","title":"Log-Likelihood","order":1},{"id":"seed_3e6a7f027161604a73bce0837d4c73eb","title":"Gradient Derivation","order":2},{"id":"seed_5f1c74d1a1c88d7a3ad3a3281e13a67a","title":"No Closed-Form Solution","order":3},{"id":"seed_71e4de39fa4b22f420d160fc24d1bc71","title":"Optimization Approach","order":4}]},{"id":"seed_177b12207ca60d43846c02b30bdfc4ae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Multi-class Logistic Regression","order":4,"contentType":"LEARNING","contentSlug":"multi-class-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cffe575643d8ccad5c8033a2fa050c1f","title":"The Softmax Function","order":0},{"id":"seed_6aa80a9b0be5561c4a4d14882fb2979e","title":"Multinomial Logistic Regression","order":1},{"id":"seed_a798362c2afa2313ccf38f5a4e029a77","title":"Cross-Entropy Loss","order":2},{"id":"seed_2e62e548b8a8c7c600a30c411da4ba2a","title":"One-vs-All Strategy","order":3},{"id":"seed_68ff5c7da93b4c9040e7096f8cf39441","title":"Computational Considerations","order":4}]},{"id":"seed_700a56229a39f2c5efb792c516b003de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Generalized Linear Models (GLMs)","order":5,"contentType":"LEARNING","contentSlug":"generalized-linear-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_61f49d503c29f3aaa9fe97060f0e84ce","title":"The GLM Framework: A Unified Theory of Regression","order":0},{"id":"seed_e5abdd3b0fefa03a337ebde0ba5d9309","title":"Link Functions: Connecting Means to Predictors","order":1},{"id":"seed_d80306c03aad79af263044607dad82e2","title":"The Exponential Family: Foundation of GLMs","order":2},{"id":"seed_4d96766c2c3d8f0b2376f3986d899f79","title":"Poisson Regression: Modeling Count Data","order":3},{"id":"seed_8f0eec87fd2cb9fac66f6b58fc301d19","title":"Negative Binomial Regression: Handling Overdispersion","order":4}]},{"id":"seed_5b5ecd2ea6321d0e366b6369851d7e1f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":6,"contentType":"VISUALIZER","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_cc24bec346a9c06530806b63a487f8db","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":7,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_94cb522600865b629be81feefa09685d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":8,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_dceafab2ca6be5ae2808a6312c2333dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Decision Tree Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"decision-tree-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_db6bac32f9c8fa3cd80510e8bd10d766","title":"Tree Structure","order":0},{"id":"seed_5c52be5dceb94348b3ce82b515f1f7b6","title":"Splitting Rules","order":1},{"id":"seed_a51f17b5d5a4d499688b830e43ca4504","title":"Leaf Predictions","order":2},{"id":"seed_7452c05ddbfdef9b478bcf2b5561d0be","title":"Recursive Partitioning","order":3},{"id":"seed_497e181ba832cf2f400fa4dd970b0051","title":"Interpretability","order":4}]},{"id":"seed_66f262a333c03eff5f0ef18781c9d3af","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Splitting Criteria","order":2,"contentType":"LEARNING","contentSlug":"splitting-criteria","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2f7e3d1f8f264924373b072cf1d7e2a6","title":"Gini Impurity: The Measure of Node Purity","order":0},{"id":"seed_1c9d698a70dc23f6de89558e90031fb7","title":"Entropy and Information Gain: The Information-Theoretic Foundation","order":1},{"id":"seed_27a597247baed7e09a847ba1c07643c3","title":"Misclassification Error: Simplicity at a Cost","order":2},{"id":"seed_8e31a4e9800e6d320ab6e525e75f99dc","title":"Comparison of Splitting Criteria: A Unified Perspective","order":3},{"id":"seed_8d0db373d93d1c18f67cd2710cedd60a","title":"Continuous Feature Splitting: Finding Optimal Thresholds","order":4}]},{"id":"seed_75b193ee8207ca9f7b2d2866489de306","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Growing Algorithms","order":3,"contentType":"LEARNING","contentSlug":"tree-growing-algorithms","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f0288d0b463a538ef9839e2b152e1fe8","title":"ID3 Algorithm","order":0},{"id":"seed_45a00dbb8f00e9e8fa5239c18fc72b75","title":"C4.5 Algorithm","order":1},{"id":"seed_acecb5290d37b28a1658150b952137c5","title":"CART Algorithm","order":2},{"id":"seed_19c3b7533c2361525fbcec0a7dc2a7d6","title":"Handling Missing Values","order":3},{"id":"seed_fa7eec5cef5065c7f8388ccd2906507b","title":"Categorical Features","order":4}]},{"id":"seed_2460c7b303e40cec69e89a72b8b831e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Pruning and Regularization","order":4,"contentType":"LEARNING","contentSlug":"pruning-and-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_841da38741645dea8f1d16766d4097c2","title":"Pre-pruning: Early Stopping Strategies","order":0},{"id":"seed_0684bbba0f126aa161a3b10443da2454","title":"Post-pruning: Cost-Complexity Pruning","order":1},{"id":"seed_7c8fec22d1e66772a261bf4b028b671d","title":"Minimum Samples: Statistical Constraints on Tree Growth","order":2},{"id":"seed_f8971d9e2c41aa674e3af7282f56f977","title":"Maximum Depth: Controlling Tree Complexity","order":3},{"id":"seed_e251446ee8db54d758a4f7a260ec06c6","title":"Minimum Impurity Decrease: Split Quality Threshold","order":4}]},{"id":"seed_f4d666cbe20d00df3882c0b2203eb2c8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Regression Trees","order":5,"contentType":"LEARNING","contentSlug":"regression-trees","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ac677a4cffa4eb4b8e7494abddfe864","title":"CART for Regression","order":0},{"id":"seed_5c865b327c14cb2ba7d85d96e8a7d6ff","title":"Splitting Criterion (MSE)","order":1},{"id":"seed_c7dd375c88d35491e1c784ea8844cfcf","title":"Leaf Predictions","order":2},{"id":"seed_b042f5d3ad71aedacdc24b113c501dbe","title":"Piecewise Constant Approximation","order":3},{"id":"seed_9443337f0d6c814c51135b3ae8d7e3be","title":"Comparison to Linear Models","order":4}]},{"id":"seed_60576ab2af9ebaac4b42cd4cfc97570c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Limitations and Extensions","order":6,"contentType":"LEARNING","contentSlug":"tree-limitations-and-extensions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c74c7553d6afe87d76c584a3baa95199","title":"Instability — The Achilles' Heel of Decision Trees","order":0},{"id":"seed_f2ffb97ca33ce7f4768bac5dcd430770","title":"Axis-Aligned Splits — The Geometric Constraint of Standard Trees","order":1},{"id":"seed_d4c206fc75437a3e8bf85b62dd8ab811","title":"Oblique Trees — Breaking the Axis-Aligned Constraint","order":2},{"id":"seed_a54f1c9c1e5fd1e0a0eaf827bab9c229","title":"Multivariate Splits — Beyond Linear Combinations","order":3},{"id":"seed_1e367502710bf09157f993258c864920","title":"Connections to Other Methods — Trees in the ML Landscape","order":4}]},{"id":"seed_5ecf05e48f7a0b21063fc124157e3b41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Recursive Classification Tree Builder","order":7,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6694bec14cdf4dec18589122c2ef6ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Maximum Margin Classifier","order":1,"contentType":"LEARNING","contentSlug":"maximum-margin-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a013e659a0da4e748d8f4fd2bc521820","title":"Geometric Margin: The Distance to Decision Boundaries","order":0},{"id":"seed_8e564baf204caedfc53ee24d6da469ce","title":"Functional Margin: The Raw Classification Score","order":1},{"id":"seed_7781928edfa2f6a8db48d6d63d0c6813","title":"Maximum Margin Objective: The Optimization Principle","order":2},{"id":"seed_99ee1607d1d433d7d55647989482db1c","title":"Support Vectors: The Critical Points","order":3},{"id":"seed_a0949455a613e6979bf3ffcd5ef97bdc","title":"Uniqueness of Solution: One Optimal Hyperplane","order":4}]},{"id":"seed_718289c9fa43b9c118e2c33a9b999fc7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Hard Margin SVM","order":2,"contentType":"LEARNING","contentSlug":"hard-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bd24e84928c4e49d5fb02c02261d93bf","title":"Optimization Problem Formulation","order":0},{"id":"seed_80314c8a4df8cf9418973501c6118eaa","title":"Quadratic Programming","order":1},{"id":"seed_441288891ef7e962e37273a2e7253aa6","title":"Lagrangian Derivation","order":2},{"id":"seed_19c4ff4e9c0b7b17c4ae1703468a3574","title":"Dual Problem","order":3},{"id":"seed_49a4b6463f97e10918e9e3d3fb8ee23a","title":"KKT Conditions","order":4}]},{"id":"seed_a42c0383f3c3f0b2cfe805c1906ef67e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Soft Margin SVM","order":3,"contentType":"LEARNING","contentSlug":"soft-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bfbd75f04834342b65318edbb84113c2","title":"Slack Variables: Tolerating Margin Violations","order":0},{"id":"seed_86a65a3533abd701056463e3dd5af455","title":"Hinge Loss: The Margin-Aware Loss Function","order":1},{"id":"seed_3354a76472b33953793b7c6fbd5b964a","title":"The C Parameter: Controlling the Margin-Error Trade-off","order":2},{"id":"seed_4b0add25426ebfe1fbb82d0bfa2f8221","title":"Dual Formulation: The Gateway to Kernel Methods","order":3},{"id":"seed_38e22c3634cc6b6e3b45c9f5be79a956","title":"Soft Margin Interpretation: The Complete Picture","order":4}]},{"id":"seed_0817db7f4e635c79befbd2ec867d4208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Kernel Support Vector Machines","order":4,"contentType":"LEARNING","contentSlug":"kernel-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dce0554e23e92f7f29832689e1e9033a","title":"Dual Form Advantage","order":0},{"id":"seed_b43d07d4cb4e2b4f7f41ecbf8e25aa74","title":"Kernel Substitution","order":1},{"id":"seed_0904460c8033632b21a801e3a4e9a9d6","title":"Common Kernels","order":2},{"id":"seed_f8e58f4809afca84d915ca2f03254da7","title":"Nonlinear Boundaries","order":3},{"id":"seed_305c4e03a791712a752734f5684cc029","title":"Kernel Selection","order":4}]},{"id":"seed_7f7f1f41aaf42cfff875745539c688c2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"SVM Optimization","order":5,"contentType":"LEARNING","contentSlug":"svm-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_541b80cdd5f2cd1733632be6b953271a","title":"Sequential Minimal Optimization (SMO) Algorithm","order":0},{"id":"seed_09139165234e636a41838eeeaddd00e4","title":"Working Set Selection","order":1},{"id":"seed_a8401f3bebe2a52cdf978b1c13f1e9a6","title":"Convergence","order":2},{"id":"seed_8754eab4b8b19725779915bae5340575","title":"Complexity Analysis","order":3},{"id":"seed_72ea95f54fd26d4cea95cd853ca0ed85","title":"Practical Implementations","order":4}]},{"id":"seed_9d6a6fae9d4733d75d9d2c0ef2bf6689","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Multi-class Support Vector Machines","order":6,"contentType":"LEARNING","contentSlug":"multi-class-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4b1f69f046d5328e99ff707789aa02a9","title":"One-vs-One (OvO) Strategy","order":0},{"id":"seed_1b7fa8150c8864265c58f0e10b9ea16b","title":"One-vs-All (OvA) Strategy","order":1},{"id":"seed_e64777f669b5bf01032b4810e325a971","title":"DAG-SVM (Directed Acyclic Graph SVM)","order":2},{"id":"seed_5223356c350f2faabbc988c6682d4ab7","title":"Crammer-Singer Formulation","order":3},{"id":"seed_423e270eb005d730e9fabd5874a5bf9c","title":"Practical Considerations for Multi-class SVM","order":4}]},{"id":"seed_2db3a376585980bc271d7b13445f187d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Gaussian Naive Bayes","order":1,"contentType":"LEARNING","contentSlug":"gaussian-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e97c002711fccb4eeeed7265c885efd3","title":"Continuous Features","order":0},{"id":"seed_ab154327f4572e2e7487faa04b589a0d","title":"Gaussian Assumption","order":1},{"id":"seed_e7e8aedb1b2e0e515d90f215b1354689","title":"Parameter Estimation","order":2},{"id":"seed_88c3971753f5766f9ca133581fc35266","title":"Decision Boundary","order":3},{"id":"seed_652624afe940275cedd923d401825e0b","title":"Connection to LDA","order":4}]},{"id":"seed_ccb1182e88952a8e41eacf6988a8f962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Linear and Quadratic Discriminant Analysis","order":2,"contentType":"LEARNING","contentSlug":"linear-and-quadratic-discriminant-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b663c68ea45a6ca1fbbb583ac9112c9b","title":"LDA Assumptions","order":0},{"id":"seed_2a1672c563bf8cb2570451b534334aaa","title":"Shared Covariance","order":1},{"id":"seed_30f3a8e32e9273fea8218dea1e695c0e","title":"QDA with Class-Specific Covariance","order":2},{"id":"seed_726eaff8e9d2a81756f201183fb245e9","title":"Decision Boundaries","order":3},{"id":"seed_ac2af5894584a1c6d2d5591f34b9eaea","title":"Regularized Discriminant Analysis","order":4}]},{"id":"seed_fbe1f6cf1357ee3876023e84eeebe2e1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Generative vs Discriminative Models","order":1,"contentType":"LEARNING","contentSlug":"generative-vs-discriminative","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c8b007934c88097a3916616249da339e","title":"Generative Model Formulation","order":0},{"id":"seed_d786ec7f230276f2cdd80df406a8046b","title":"Discriminative Model Formulation","order":1},{"id":"seed_e3ab381760a46322c1bacf38bef47bbc","title":"Pros and Cons: A Comprehensive Comparison","order":2},{"id":"seed_1625278a8125ba9bbac6b390ac550c83","title":"When to Use Each: Practical Decision Framework","order":3},{"id":"seed_dcc3442b4449518a1ae900f3f27db1f3","title":"The Famous Debate: Ng & Jordan (2001)","order":4}]},{"id":"seed_1cc60ba5baf88bab74e808a1eb6e245f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Bayes Classifier","order":2,"contentType":"LEARNING","contentSlug":"bayes-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3d10da113ce802c8a852c5b96c4aad7a","title":"Bayes Decision Rule","order":0},{"id":"seed_36207040873f9070196a6f07f1070292","title":"The Optimal Classifier","order":1},{"id":"seed_e359cdddffb130d0cf1a2816e38a0a4c","title":"Bayes Error Rate","order":2},{"id":"seed_732f83c1286963163d871881937c9e2f","title":"Posterior Computation","order":3},{"id":"seed_6d9701ee09ac8c68d18443eeaf6b9e2f","title":"Practical Challenges","order":4}]},{"id":"seed_b88cd36a22d2096598e1f8434cad7cbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Naive Bayes Assumption","order":3,"contentType":"LEARNING","contentSlug":"naive-bayes-assumption","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_71a76992bb037b07b38fff7e6be4d220","title":"Conditional Independence","order":0},{"id":"seed_e10c8c25e6a31d2df94595f86a47e1f8","title":"When the Assumption Holds","order":1},{"id":"seed_f5aa87e2cf27bc16e0787c8b3e414288","title":"When the Assumption Fails","order":2},{"id":"seed_301dba405088214629dda64de083094e","title":"Still Works Surprisingly Well","order":3},{"id":"seed_bb9a2adbeda94ac7dc465728342e67ea","title":"Theoretical Analysis","order":4}]},{"id":"seed_c827e845d309186b4d828114f01bf6ea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Multinomial and Bernoulli Naive Bayes","order":4,"contentType":"LEARNING","contentSlug":"multinomial-bernoulli-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7039ddc6e6dec09f66328dae7cd724e1","title":"Text Classification","order":0},{"id":"seed_66282f75590576422d0c8b6bbf93d88e","title":"Multinomial Naive Bayes Model","order":1},{"id":"seed_58ae07ae636beb76783fa9de70eed98d","title":"Bernoulli Naive Bayes Model","order":2},{"id":"seed_d6ab95eb1edb7c82c2baae5c49129b10","title":"Laplace Smoothing","order":3},{"id":"seed_f5eae1287c6ed688b015bc60cc1ad757","title":"Comparing Multinomial and Bernoulli Naive Bayes","order":4}]},{"id":"seed_8e0dbdfdcaf7222bdf2b6330b80049eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Algorithm","order":1,"contentType":"LEARNING","contentSlug":"knn-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b3ef7c78abad802522bced6f8b593088","title":"Instance-based Learning","order":0},{"id":"seed_92702947917f121e7491381b08b81653","title":"Lazy Learning","order":1},{"id":"seed_b3a1d09a31edd56725996d96a4b3ec49","title":"K Selection","order":2},{"id":"seed_458eeb6b81ba4009afd003599aa716b2","title":"Voting Schemes","order":3},{"id":"seed_de99956f6dd8c49e6334359c9e1dc045","title":"Algorithm Pseudocode","order":4}]},{"id":"seed_9a886703f21dde8f994accd7696e3962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Distance Metrics for K-Nearest Neighbors","order":2,"contentType":"LEARNING","contentSlug":"distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cc37f06bb61f5ab2554947a5d8f04678","title":"Euclidean Distance","order":0},{"id":"seed_3d7c996c0693354f6ee63a0f9951377f","title":"Manhattan Distance","order":1},{"id":"seed_c10355bdf63bb5a21e338f040119a32c","title":"Minkowski Distance","order":2},{"id":"seed_0215ac1dc6550fbb45632681d85c8c7f","title":"Cosine Similarity","order":3},{"id":"seed_5c498fed2336c119293ffe28ccb9f584","title":"Custom Distances","order":4}]},{"id":"seed_beabe40abd8e31e1a3461563e488e5ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Curse of Dimensionality","order":3,"contentType":"LEARNING","contentSlug":"curse-of-dimensionality","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a25701afefc8d1c19f74621dd381cac2","title":"Distance Concentration","order":0},{"id":"seed_9793aa996f0eb675f6de731a34b2c580","title":"Sparsity in High Dimensions","order":1},{"id":"seed_5fb07d6347eef101bae630aeb875fc22","title":"Volume of Hyperspheres","order":2},{"id":"seed_f3f2bbd9c93fe19dd118e4f9a2846696","title":"Implications for KNN","order":3},{"id":"seed_dcdd97dc500b5459250187469a9bdc5f","title":"Mitigation Strategies","order":4}]},{"id":"seed_a3e281d02cea4be01efa2e8de9d21650","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Efficient K-Nearest Neighbors","order":4,"contentType":"LEARNING","contentSlug":"efficient-knn","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_767b8623bade70774755f07ea7b14276","title":"Brute Force Complexity","order":0},{"id":"seed_f3992bc86691be1608de8b1c11595164","title":"KD-Trees (Construction and Search)","order":1},{"id":"seed_9439553638b58a064b81b1a0a669df26","title":"Ball Trees","order":2},{"id":"seed_8820b1217773d0e969c6fbff1d27fd03","title":"Voronoi Diagrams","order":3},{"id":"seed_b7a48c9ece938fe502ba756a6f8da019","title":"Locality-Sensitive Hashing (LSH)","order":4},{"id":"seed_b0c19ec065e24656574a793db5afeeb3","title":"Approximate Nearest Neighbors","order":5}]},{"id":"seed_48ce033aa3506c796b6262f9edcf55a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Voronoi Diagrams & Decision Surfaces","order":5,"contentType":"LEARNING","contentSlug":"voronoi-diagrams-and-decision-surfaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ed15082a903245caac2646e9f1b0707d","title":"Voronoi Tessellation","order":0},{"id":"seed_9f686904baeb073c51ffd116b1e55173","title":"Decision Boundaries as K Changes","order":1},{"id":"seed_48e91cb238d93f16b350b58847790ade","title":"1-NN Creates Voronoi Cells","order":2},{"id":"seed_d93d8af8669b7b1bf2d0b1515c1db018","title":"Visualization Techniques","order":3},{"id":"seed_899ce2078ad4c1fbfcb5437be630e0f5","title":"Decision Surface Smoothing with K","order":4}]},{"id":"seed_4186620863285c1d7b3ce960bbd55ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Locality-Sensitive Hashing (LSH)","order":6,"contentType":"LEARNING","contentSlug":"locality-sensitive-hashing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_422fc067c242a6c1592bfdc49657350d","title":"Hashing vs Locality-Sensitive Hashing","order":0},{"id":"seed_987cdd3db17aeb065a3b73d1748b3ab2","title":"LSH for Cosine Similarity","order":1},{"id":"seed_11e00ed6e7f9795dae476b69160f939d","title":"LSH for Euclidean Distance","order":2},{"id":"seed_b0274b47970102b6f6a1bc16dbb7765a","title":"Hash Function Families","order":3},{"id":"seed_b00b68cd39352ab9b7cc9a215ab64bf3","title":"Collision Probability Analysis","order":4},{"id":"seed_4ae869636b1a7b8510688cf03f021a04","title":"LSH Performance Tuning","order":5}]},{"id":"seed_02e815b47429e576631e0381220c4966","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Weighted KNN","order":7,"contentType":"LEARNING","contentSlug":"weighted-knn","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c0d21453491f6814f6c1e82b7cdb03fa","title":"Distance-Weighted Voting","order":0},{"id":"seed_0c77828567376cbec542352769b007c3","title":"Kernel-Weighted KNN","order":1},{"id":"seed_367df2ca69735b580bcf7d45c917f771","title":"Adaptive Neighborhoods","order":2},{"id":"seed_7a1bb2b4d7b064b03b362fc25377e69e","title":"Local Models","order":3},{"id":"seed_979f6a7e5374163fd77e264b1f8e30fd","title":"Regression with KNN","order":4}]},{"id":"seed_63ff05a505bafec47763bc44bff3a983","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Variants","order":8,"contentType":"LEARNING","contentSlug":"knn-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_852693e826b7b366fc698220dd5ab126","title":"Condensed Nearest Neighbors","order":0},{"id":"seed_396e2e4b3c6afabaf32e843498ccd1e4","title":"Edited Nearest Neighbors","order":1},{"id":"seed_ed592d7b5e978e26879209ca7e2c959e","title":"Large Margin K-Nearest Neighbors","order":2},{"id":"seed_15ae9066457b0afbe32ad7e477f75ffe","title":"Metric Learning","order":3},{"id":"seed_880c94be4a738368403f33ec2a2af9df","title":"KNN Ensemble Methods","order":4}]},{"id":"seed_bafb1e8a6af32317a42e273f5a15de1c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Ensemble Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"ensemble-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d5ae93f6a322acdc0c106281d92ad6d9","title":"Why Ensembles Work","order":0},{"id":"seed_bb68e302e90e329001dbbf2117b0c1d0","title":"Wisdom of Crowds","order":1},{"id":"seed_c7e0c238cbbeaccc7f7ef573b1357249","title":"Error Decomposition","order":2},{"id":"seed_d7d99c150d01c767dd84ce792359a169","title":"Diversity Requirement","order":3},{"id":"seed_237fc0e1ef894fc4db67f7b70178939a","title":"Ensemble Strategies","order":4}]},{"id":"seed_22c2fdccde0ab9e52f64339f364e757d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Random Forests","order":2,"contentType":"LEARNING","contentSlug":"random-forests","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f206c4e9769ba52a5a5337678ddba40a","title":"Random Subspace Method","order":0},{"id":"seed_7c8554e88c301160e491ad7f2045acfd","title":"Feature Randomization","order":1},{"id":"seed_36571f6077f8546d0f635286e5d023e0","title":"Tree Correlation Reduction","order":2},{"id":"seed_0111e6c4513c2938886ae44dd6609f70","title":"Hyperparameters","order":3},{"id":"seed_5dbd3c3660e45b6890f624d91c2f8774","title":"Parallelization","order":4}]},{"id":"seed_00a4486bae311423e333adfc96315696","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Bagging for Other Models","order":3,"contentType":"LEARNING","contentSlug":"bagging-for-other-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f17b473b206d45a4963ce1982e311cd8","title":"Bagged Decision Trees","order":0},{"id":"seed_d2546d82a8e25fd32de0addc8eac9c22","title":"Bagged Neural Networks","order":1},{"id":"seed_2800b27da94dd7e25ee1ef83acaecf69","title":"Model Aggregation Strategies","order":2},{"id":"seed_f74c790d98bfc3f02c4a1b2dc1767bae","title":"Bagging vs Boosting","order":3},{"id":"seed_e271e771407a1ebbcf2227430171665e","title":"When Bagging Helps","order":4}]},{"id":"seed_c51429550b08bde8708ef8c1c162ef1d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Bootstrap Aggregating: Variance Reduction Through Ensemble Averaging","order":1,"contentType":"LEARNING","contentSlug":"bootstrap-aggregating","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6c0316bc295dad900b4c9708c20a72fc","title":"Bootstrap Sampling: The Statistical Foundation of Bagging","order":0},{"id":"seed_d0435232560eee440ff892d090415ddb","title":"Aggregation Methods: Combining Predictions for Ensemble Power","order":1},{"id":"seed_6f1f9995617ac89e722490361c1d689d","title":"Variance Reduction: The Mathematical Heart of Bagging","order":2},{"id":"seed_bca67afd5ce45056b9c8e71e3adba2da","title":"Bias Preservation: Why Bagging Doesn't Increase Systematic Error","order":3},{"id":"seed_713633bd98f3959d3237ce13d9456cf2","title":"Out-of-Bag Estimation: Free Validation in Bagged Ensembles","order":4}]},{"id":"seed_51f41e3368033fc0b10899ff25d5c5ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Feature Importance","order":2,"contentType":"LEARNING","contentSlug":"feature-importance","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ce10f972faaec2f14d6203a335fd01ab","title":"Impurity-based Importance","order":0},{"id":"seed_02f98b069cefc31e4c0b227570f23b0b","title":"Permutation Importance","order":1},{"id":"seed_a3d2cc9f29a4b681486ab2e469a5c07f","title":"Drop-Column Importance","order":2},{"id":"seed_b55d0c9d2b705a96995dc4079b9b43a2","title":"Bias in Importance","order":3},{"id":"seed_274a631bff1b01c8032fca70fb006813","title":"Interpretation Guidelines","order":4}]},{"id":"seed_5ad2b982504501af216bbc78e377ae2a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Random Forest Variants","order":3,"contentType":"LEARNING","contentSlug":"random-forest-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e78bf7e2fdf7e3ebd10936630916d089","title":"Extremely Randomized Trees (Extra-Trees)","order":0},{"id":"seed_8f6d7cf5fab415ae55ddc7f281342bf0","title":"Rotation Forests","order":1},{"id":"seed_51ec2c44de67b1623dc9979d615c6c3c","title":"Random Patches","order":2},{"id":"seed_c8d2077b09ada53e477b319fee18d7a8","title":"Subspace Forests","order":3},{"id":"seed_3f5f370912bce10fec92dd4f33906f54","title":"Oblique Random Forests","order":4}]},{"id":"seed_f03dc46090058acd74b559494ee32f72","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting Intuition","order":1,"contentType":"LEARNING","contentSlug":"boosting-intuition","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_98ca86a4e9390242ea140586859fb175","title":"Weak Learners","order":0},{"id":"seed_eef1b969f20d08017b67936fe0520739","title":"Sequential Combination","order":1},{"id":"seed_7021c8a2344039ec4656418c52002283","title":"Focus on Errors","order":2},{"id":"seed_e00ae9c3b7d46fa2b68612e9048a83ad","title":"Strength Amplification","order":3},{"id":"seed_9c8238d5d70f52460347f19ed7f9f86e","title":"Historical Development","order":4}]},{"id":"seed_013f75c45c26c5238649f80602c7eea3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"AdaBoost: Adaptive Boosting","order":2,"contentType":"LEARNING","contentSlug":"adaboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9f9509db19eb4a045548fb42acc478a4","title":"Adaptive Boosting Algorithm","order":0},{"id":"seed_b0ef2a1154c2561572a94854fcee8d64","title":"Weight Updates","order":1},{"id":"seed_c7c9c97ada179ecb486c200accc4fc6f","title":"Weighted Voting","order":2},{"id":"seed_348190b4ed209df5ea6f490204fe09a9","title":"Training Error Bounds","order":3},{"id":"seed_24e976d10f8f97156a4382c0db3dca8f","title":"Exponential Loss Connection","order":4}]},{"id":"seed_747bf0ebc82590cbd3552b19fb0b76e9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting as Gradient Descent","order":3,"contentType":"LEARNING","contentSlug":"boosting-as-gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ce231cc963a00c8d9cdec279d1c21fa6","title":"Functional Gradient Descent","order":0},{"id":"seed_631a9be40b4f0e974be95aea0c162dca","title":"Additive Models","order":1},{"id":"seed_c913caed106b3fd4ca17d8670d868a34","title":"Stage-wise Optimization","order":2},{"id":"seed_322dc9aa1b121b02998bb0bab670965a","title":"Loss Function Formulation","order":3},{"id":"seed_c66dcc7ae3692a748ffb079bd37409f8","title":"Forward Stagewise Additive Modeling","order":4}]},{"id":"seed_51f106abb54ddee63c6fb37b54ff5b4a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Gradient Boosting","order":4,"contentType":"LEARNING","contentSlug":"gradient-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_79ce96b4b51b651908940c2ba9fb85c1","title":"Gradient Boosting Algorithm","order":0},{"id":"seed_df76c44846278e19cd9b1c54ff1885a9","title":"Pseudo-Residuals","order":1},{"id":"seed_2e7b77b7bce0d05f2fcde734ea026212","title":"Base Learner Fitting","order":2},{"id":"seed_83366531d92b0d16baccc7e3488d7bfc","title":"Step Size (Learning Rate)","order":3},{"id":"seed_ba13e031492bda2444d32a7f65e054d9","title":"Stopping Criteria","order":4}]},{"id":"seed_dc5adc297f1955b0a18ca701a518df00","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Loss Functions for Boosting","order":5,"contentType":"LEARNING","contentSlug":"loss-functions-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c3eeae4b8adb94712ed56dc03190aff8","title":"Squared Loss (Regression)","order":0},{"id":"seed_fed01c3688f0c351a239bdde913abbca","title":"Absolute Loss","order":1},{"id":"seed_e5bacf98767f3866627c074cf0efacdf","title":"Huber Loss","order":2},{"id":"seed_96cac975c288f2256792a91f178807d2","title":"Logistic Loss (Classification)","order":3},{"id":"seed_556e7382a179e706571235d8bb2d391b","title":"Custom Loss Functions","order":4}]},{"id":"seed_8af9b78e83ce53d210e580fa87ed569b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Regularization in Boosting","order":6,"contentType":"LEARNING","contentSlug":"regularization-in-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a836d91b120a3264e891b66c91ecda00","title":"Shrinkage (Learning Rate)","order":0},{"id":"seed_bab2ca4cb3d208d4ecbc44a456c0754d","title":"Subsampling (Stochastic Gradient Boosting)","order":1},{"id":"seed_aa794853cc8545c3fe9ddcfd322e7077","title":"Tree Constraints","order":2},{"id":"seed_2e6574ef7caadb7315d805c32fc2271e","title":"Early Stopping","order":3},{"id":"seed_eaee9b00d47ee8d6aa36b6933e09d5c0","title":"L1/L2 Regularization","order":4}]},{"id":"seed_fb17f19632cfed0944218bf0287a019f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"XGBoost: Extreme Gradient Boosting","order":1,"contentType":"LEARNING","contentSlug":"xgboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_70199ea37552e815d30cd1e4e1e1a96e","title":"Regularized Objective","order":0},{"id":"seed_fc960362e857ca3253deb02648981ebc","title":"Second-Order Approximation","order":1},{"id":"seed_3aa4db9638718d1f047e9ce3c5958c99","title":"Efficient Split Finding","order":2},{"id":"seed_ec0b115e474f81dce25b417d03408843","title":"Sparsity-Aware Algorithm","order":3},{"id":"seed_a6ddcd6fe2a2752c06d1caa528c409f9","title":"System Optimizations","order":4}]},{"id":"seed_f4ed4ac2f15096908a3839a371aea45d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"LightGBM: Light Gradient Boosting Machine","order":2,"contentType":"LEARNING","contentSlug":"lightgbm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b372e5bb5fe0d674a5dcc204db08433b","title":"Leaf-wise Tree Growth","order":0},{"id":"seed_12dce22898a5635acc15a2926974f893","title":"Gradient-based One-Side Sampling (GOSS)","order":1},{"id":"seed_b0c60ddce5a674de0633f91754a7ca0f","title":"Exclusive Feature Bundling (EFB)","order":2},{"id":"seed_4838bfc1b22480cc4ffb5bfb5ee5f893","title":"Histogram-based Splitting","order":3},{"id":"seed_2a537c9c9e3a285c0545625ab68f3224","title":"Speed Comparison","order":4}]},{"id":"seed_2c9e0dfc3b67f3a1c9330f30e269636b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"CatBoost","order":3,"contentType":"LEARNING","contentSlug":"catboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3930f8b1ed807d4ece32050803e0f427","title":"Ordered Boosting","order":0},{"id":"seed_c83ee840f4e102e8edbc47fe0327c551","title":"Categorical Feature Handling","order":1},{"id":"seed_0f63c88bbb221b89e0692e9225b69c0b","title":"Target Statistics","order":2},{"id":"seed_899aaf6e82fd390d644516dc0f937818","title":"Symmetric Trees","order":3},{"id":"seed_6c6b606e013767ff1459da935a9b6731","title":"GPU Acceleration","order":4}]},{"id":"seed_8a969e8156debd6e794f66d4ba1b8f47","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Hyperparameter Tuning for Boosting","order":4,"contentType":"LEARNING","contentSlug":"hyperparameter-tuning-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ab93c270234af71b045e0331980ae611","title":"Key Hyperparameters","order":0},{"id":"seed_d900530b9fe873a5a86ec75b05b1c723","title":"Learning Rate and Iterations","order":1},{"id":"seed_e144a6ff394ec3a69eecbf7297ec4f93","title":"Tree-Specific Parameters","order":2},{"id":"seed_c78b2620e23f30ec5c56f818a8bff473","title":"Regularization Parameters","order":3},{"id":"seed_c6e2bc3a0f056809e3191424e6a4bf34","title":"Tuning Strategies","order":4}]},{"id":"seed_b3cc7ecb9af183e57d91f1d32acd0ef2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Feature Engineering for Boosting","order":5,"contentType":"LEARNING","contentSlug":"feature-engineering-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1ad793ee0751b514ebf7d490b1b952fc","title":"Feature Interactions","order":0},{"id":"seed_bb14d8bf8c61fc7a57de9ef09da35e70","title":"Target Encoding","order":1},{"id":"seed_5f41f5b1d125bd6f335516a6bfb2f71f","title":"Frequency Encoding","order":2},{"id":"seed_a07c750ff7cb96a3ff84ad2a324cef28","title":"Categorical Handling","order":3},{"id":"seed_6fa2913074e9d9a1163d3b94ebfab7aa","title":"Feature Selection","order":4}]},{"id":"seed_0f0a3fcb7bd62041bb7bef139cbd5bdc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Boosting Best Practices","order":6,"contentType":"LEARNING","contentSlug":"boosting-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0cfac23065070fd5a2a40b9d073376a7","title":"Training Monitoring","order":0},{"id":"seed_9319321188b2e2866f475f7a2d580793","title":"Early Stopping","order":1},{"id":"seed_cc82cb532dc200977d249bf5f116ae94","title":"Cross-Validation for Boosting","order":2},{"id":"seed_428b7a4e049d1d41fcebaa026fcd138b","title":"Handling Class Imbalance","order":3},{"id":"seed_ef87b51f42063bf9d403fdf614938ce3","title":"Production Deployment","order":4}]},{"id":"seed_ac4e81c68db8fe1d9ac89997df18a501","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_eb79d19a52a1cef8a8a8cdd2426f916e","title":"Confusion Matrix","order":0},{"id":"seed_56c6db1f8bc543fa0e141e4e0b025ab8","title":"Accuracy and its Limitations","order":1},{"id":"seed_b23510959d7f2a185c7b18e3741c0e3f","title":"Precision and Recall","order":2},{"id":"seed_b2492dc4263da4b3287d9816c6063289","title":"F1 and F-beta Scores","order":3},{"id":"seed_5d4b7593b2bca1a772cfb84582db8f7e","title":"Specificity and Sensitivity","order":4}]},{"id":"seed_c5ef7edae79ba6a58e3fd0d44017ac94","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"ROC and Precision-Recall Curves","order":2,"contentType":"LEARNING","contentSlug":"roc-and-precision-recall-curves","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f336d1c844076978572127851b8365a4","title":"ROC Curve Construction","order":0},{"id":"seed_6c185b4df6bbfc5861624f6a99c35d55","title":"AUC Interpretation","order":1},{"id":"seed_c8833f1a7cd747239aaaefe2d034f66b","title":"Precision-Recall Curves","order":2},{"id":"seed_dd54720bb9d55472ee7eb3c9d050c699","title":"Average Precision","order":3},{"id":"seed_21b6caddc825af8a012d9a429ef99953","title":"Curve Comparison","order":4}]},{"id":"seed_7e80b225bee84e82f8ac145be8ea9c03","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Multi-class Classification Metrics","order":3,"contentType":"LEARNING","contentSlug":"multi-class-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_76d1743edf649c1ef84825ac26b4c856","title":"Micro vs Macro Averaging","order":0},{"id":"seed_f498477cafa16cc6d7648ac6982793a1","title":"Weighted Averaging","order":1},{"id":"seed_629c2a58a1666636cabb0d6f0f31c9c4","title":"Cohen's Kappa","order":2},{"id":"seed_014845c4745e9003c80cc069f6615ba7","title":"Multi-class Confusion Matrix","order":3},{"id":"seed_e812f4c29eb13b5f0e5be48c5b12003c","title":"One-vs-Rest Metrics","order":4}]},{"id":"seed_4d46bb09037b0706babb1caba43f8b9d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Regression Metrics","order":4,"contentType":"LEARNING","contentSlug":"regression-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ff948bafce2f5d7edf0cb72789b7b84e","title":"Mean Squared Error","order":0},{"id":"seed_54ea2f6fa589871cf4c5992834e38ffd","title":"Mean Absolute Error","order":1},{"id":"seed_b226c87c236216b00dbd2e1dd17e2e14","title":"R-squared and Adjusted R²","order":2},{"id":"seed_86e6589094d7fbf4e3a28dbcfbcbf734","title":"MAPE and SMAPE","order":3},{"id":"seed_7e5d0a4572c4bfe5f7bb65507738a911","title":"Median Errors","order":4}]},{"id":"seed_43ad46d64dbce2d1516e1fb067ceaf9a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Ranking Metrics","order":5,"contentType":"LEARNING","contentSlug":"ranking-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_84f6b9a7d3fb2640cd307846c84b0d3d","title":"Precision@k and Recall@k","order":0},{"id":"seed_221dabfb9032e726242711c9a617c9d8","title":"Mean Average Precision","order":1},{"id":"seed_66b42dd4453f588606e8bb44f8208610","title":"Normalized Discounted Cumulative Gain (NDCG)","order":2},{"id":"seed_c66f91f7f3c243dc416a982d3e5ebea2","title":"Mean Reciprocal Rank","order":3},{"id":"seed_8c99d7299835e38dda06ea4fab4fd2a8","title":"Partial AUC (pAUC)","order":4}]},{"id":"seed_817f0073a4cafb13f8f380c749e23c80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Custom and Business Metrics","order":6,"contentType":"LEARNING","contentSlug":"custom-and-business-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3751bbcee6cb0b764da38c971ea6ee9c","title":"Cost-Sensitive Evaluation","order":0},{"id":"seed_560e5e44206a74694e8bea896fe27329","title":"Threshold Optimization","order":1},{"id":"seed_a60752395d179471ef3a0f088943fbb9","title":"Business Metric Alignment","order":2},{"id":"seed_f60b124657f70409c9629164a01d6a28","title":"Multi-objective Evaluation","order":3},{"id":"seed_21b19afeef2489bc3382cb5a3da77f9f","title":"Metric Selection Strategy","order":4}]},{"id":"seed_d59e5e1e4d128d743b25c62154ab3fbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Softmax Probability Distribution","order":7,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_347487ee43f17af456b511a19a40dca7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Holdout Validation","order":1,"contentType":"LEARNING","contentSlug":"holdout-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_96f58974343fea0b99df7e45d06569f4","title":"Train-Test Split","order":0},{"id":"seed_79ce601fb6fb7e631a58b96376a8bebf","title":"Validation Set","order":1},{"id":"seed_996447f4b323ec6f15878c037211c300","title":"Stratification","order":2},{"id":"seed_9d1b394b638941f99e49558d9d436181","title":"Random Seeds","order":3},{"id":"seed_6ce67226b5c4b9cccbbc93c6f529015b","title":"Limitations","order":4}]},{"id":"seed_e0201302cbabe1c8ac59d7eb9fd7b3b5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Stratified and Group Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"stratified-and-group-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d4926b31a50af824bf28a2e12e0f9e51","title":"Stratified K-Fold Cross-Validation","order":0},{"id":"seed_364fdc1c9f8ff7fb22150a9d7c3a4c96","title":"Group K-Fold Cross-Validation","order":1},{"id":"seed_186dc21adde3df5ac469dfc659c16c21","title":"Leave-One-Group-Out Cross-Validation","order":2},{"id":"seed_69767da960f2ef1284e12f7d69c07111","title":"Grouped Stratification","order":3},{"id":"seed_aeec593746900035c25f344de4612889","title":"Data Leakage Prevention","order":4}]},{"id":"seed_5817575eae860da4b31aff4fbbe86bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Bootstrap Methods","order":3,"contentType":"LEARNING","contentSlug":"bootstrap-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9fceb4e5cc593883fca0f5f9c75e086a","title":"Bootstrap Resampling","order":0},{"id":"seed_b37adf0dd1807da5b4259cabf9cd93b4","title":"The .632 Bootstrap Estimator","order":1},{"id":"seed_298028604136134913e79b0b2a571816","title":"The .632+ Bootstrap Estimator","order":2},{"id":"seed_05835f610bc7e373701b43d84b574e21","title":"Out-of-Bootstrap Error","order":3},{"id":"seed_b8da235d9e7370cedf8d0c0b3f04a46c","title":"Bootstrap Confidence Intervals","order":4}]},{"id":"seed_0a5077e15e4a5548c820847a82b9c107","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"K-Fold Cross-Validation","order":1,"contentType":"LEARNING","contentSlug":"k-fold-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4a5fa508a5702be89513ce945d89b128","title":"K-Fold Procedure","order":0},{"id":"seed_7e78d108e68d55ea1e174eef9123547e","title":"Bias-Variance Tradeoff in Cross-Validation","order":1},{"id":"seed_cdae615f0ebafe7ed72873f5c0046608","title":"Choosing K","order":2},{"id":"seed_adaf5c84caed1e7f53c0ef02bbfa4ba3","title":"Repeated Cross-Validation","order":3},{"id":"seed_2d6a6d9e303f07f3098a304cef3c07ef","title":"Confidence Intervals","order":4}]},{"id":"seed_80bb08d6076b67b40673dbd30ce30996","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Time Series Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"time-series-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c96bf1c05d2725ec3d24f7f73d4cafe2","title":"Forward Chaining","order":0},{"id":"seed_8908ba532db02160ad9a7166a71e9723","title":"Sliding Window Cross-Validation","order":1},{"id":"seed_b2cd7b69fc0fb3be06564ab70e878e37","title":"Expanding Window Cross-Validation","order":2},{"id":"seed_870c537e8969d11394dae89743483b75","title":"Embargo Periods in Time Series Cross-Validation","order":3},{"id":"seed_b752a005c18c1ae028fcf9dbf5849938","title":"Purging and Embargo: Complete Leakage Prevention","order":4}]},{"id":"seed_4caea31c12e70b94b3449288ee507316","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Nested Cross-Validation","order":3,"contentType":"LEARNING","contentSlug":"nested-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c869f65db45ee86e76fa7daa0815fb55","title":"Model Selection Bias","order":0},{"id":"seed_3d35bdc7ab663734b82644477f2c49b0","title":"Inner and Outer Loops","order":1},{"id":"seed_1ccc0553096bdced92e308c72e75f469","title":"Unbiased Performance Estimation","order":2},{"id":"seed_e91b35fae33bd40610ac6ca2e1240e3f","title":"Computational Cost","order":3},{"id":"seed_dcdade91f4793997990cbd8f185aeeec","title":"When to Use Nested CV","order":4}]},{"id":"seed_c39b44e15b86f86fb46fad967dc035f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Data Partitioning with Cross-Validation Splits","order":4,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_a7879f98db25ce0269f64ef3f5fe4bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Hyperparameter Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"hyperparameter-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c1727a878fd60a753256e7ffaecec1a7","title":"Model vs Hyperparameters","order":0},{"id":"seed_ae608fd9f33bc5ddcb24d24ff4fcbae6","title":"Search Space Definition","order":1},{"id":"seed_ff9d725b4d4a85fa6dedd91cd9ca7398","title":"Continuous vs Discrete Hyperparameters","order":2},{"id":"seed_5ae97fec603191f7b453a32729c14de9","title":"Conditional Hyperparameters","order":3},{"id":"seed_de0e7c2c2fb503a7450f39bf0f06abfc","title":"Hyperparameter Importance","order":4}]},{"id":"seed_94e0b48f0fb220a06f9766ad9d913f80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Grid Search","order":2,"contentType":"LEARNING","contentSlug":"grid-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6209f17483fc1db5ff70ef7f333eeba0","title":"Exhaustive Search","order":0},{"id":"seed_c874efee6a2b210b72bdc030260cdaf4","title":"Computational Cost","order":1},{"id":"seed_e37f5215b680d17a0c9cea1d6125eb21","title":"Curse of Dimensionality","order":2},{"id":"seed_cbad045ce9b80711bfe55509f5e22f74","title":"Practical Guidelines","order":3},{"id":"seed_f12c9ef59361fc98cb0f7e75794af995","title":"When Grid Search Works","order":4}]},{"id":"seed_b3ebbda137c88b6799101f8804197d6d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Random Search","order":3,"contentType":"LEARNING","contentSlug":"random-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b8dbd02d376b936535d1107ec2d95803","title":"Random Sampling","order":0},{"id":"seed_9b13f5f0943eebb698f67d8d64ccc3ee","title":"Why Random Beats Grid","order":1},{"id":"seed_e2d85c7a5e32f8659ba80d3d291c93d5","title":"Coverage Probability","order":2},{"id":"seed_56890884b5e61e26e9b61fe35c25d7a6","title":"Practical Advantages","order":3},{"id":"seed_20028fce4f2027a0c041515776790a47","title":"Parallelization","order":4}]},{"id":"seed_eb2d1d9b92ee8ca33315169f186c57a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Bayesian Optimization","order":4,"contentType":"LEARNING","contentSlug":"bayesian-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a66599dc40fa860a585f65cd5071eab0","title":"Sequential Model-Based Optimization","order":0},{"id":"seed_47b354e8d6cc19a442bc7125d76ada91","title":"Surrogate Models: Gaussian Processes","order":1},{"id":"seed_04d556ba4dbf7d1a640ccc10011464b5","title":"Acquisition Functions","order":2},{"id":"seed_bc42f88421c3b49d91e9b742d9c14e07","title":"Exploration-Exploitation Tradeoff","order":3},{"id":"seed_6bc328fe1353478592e63c06564c3e2b","title":"Tree-structured Parzen Estimators (TPE)","order":4}]},{"id":"seed_217594c65573968f0c532e09b11d0155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Multi-Fidelity Optimization","order":5,"contentType":"LEARNING","contentSlug":"multi-fidelity-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f1dd2aefb4623e0f136b03d1a9fd468e","title":"Early Stopping Approaches","order":0},{"id":"seed_73657a73228ebc847985937d21fd986b","title":"Successive Halving","order":1},{"id":"seed_bd255231e218ccc5d18f5638e4c50b0d","title":"Hyperband","order":2},{"id":"seed_f4a024c7b2a88330c0f12654003dbff7","title":"BOHB: Bayesian Optimization and Hyperband","order":3},{"id":"seed_ab7e60ffac3c3caaf774ec88eed94491","title":"Budget Allocation","order":4}]},{"id":"seed_cd56aef607f08e69d72c3e7d013c7a26","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Advanced HPO Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-hpo-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ca22bfaaef6ad00aca2a8d0e34a0ab40","title":"Neural Architecture Search","order":0},{"id":"seed_009bd7c79344b542aa9a674509f6c02c","title":"Meta-Learning for HPO","order":1},{"id":"seed_1cbece4390cf0830651828b99278c979","title":"Transfer HPO","order":2},{"id":"seed_ea8fcd74956aac3f7e319e66725b90e9","title":"Multi-Objective HPO","order":3},{"id":"seed_9f46eb66582c82266b5ef05b90678c14","title":"Practical HPO Systems","order":4}]},{"id":"seed_dfc3625133e375f2ddb7281b904bc083","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Problem Formulation","order":1,"contentType":"LEARNING","contentSlug":"clustering-problem-formulation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b089f19bab925ede1a5a7f526be2e789","title":"Clustering Objectives","order":0},{"id":"seed_5304cef754736ff2c73bf9e8874fa25b","title":"Similarity and Distance","order":1},{"id":"seed_1615fd35e02428f5d2c5a573e26ab35c","title":"Cluster Definitions","order":2},{"id":"seed_31f34316ffaa4b3baf2fa63656a370f6","title":"Types of Clustering","order":3},{"id":"seed_f062a33054e42f50b4f51c6adbc7ed1e","title":"Evaluation Challenges","order":4}]},{"id":"seed_677d6ef0d11ced3abb40fe90153268f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Clustering","order":2,"contentType":"LEARNING","contentSlug":"k-means-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1e3f708a5168d3548eb39e633bfa7a1d","title":"Lloyd's Algorithm","order":0},{"id":"seed_a4a8bdb288e0f48b56be276f51be91a8","title":"Objective Function","order":1},{"id":"seed_89c17964401784133112986793dda6a7","title":"Initialization Strategies (K-Means++)","order":2},{"id":"seed_c10fb36d3e93c249e4720cc30f5b1e44","title":"Convergence Properties","order":3},{"id":"seed_4f8b3d4dfcb5421805762e0aa52f868f","title":"Limitations","order":4}]},{"id":"seed_d3f71756678ae1cb6211450c7d2ca5ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Variants: Beyond Standard K-Means Clustering","order":3,"contentType":"LEARNING","contentSlug":"k-means-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ecfd11f888404c7f61b0bbd5ff07640e","title":"K-medoids (PAM): Robust Clustering with Actual Data Points","order":0},{"id":"seed_2e01fc478d63e0ddaee892059d751b3d","title":"K-medians: Robust Clustering with L1 Distance","order":1},{"id":"seed_9ae17bff6520c9a70120ad5f163f6171","title":"Fuzzy C-means: Soft Clustering with Degrees of Membership","order":2},{"id":"seed_801a5b873b7725088678a6f4d8143249","title":"Mini-batch K-means: Scalable Clustering for Large Datasets","order":3},{"id":"seed_c9bb3c0be14c9a44a5b9fc642f979f62","title":"Bisecting K-means: Divisive Hierarchical Clustering","order":4}]},{"id":"seed_ad45ea7d5ec46859780dfb1e1b634e18","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Hierarchical Clustering","order":4,"contentType":"LEARNING","contentSlug":"hierarchical-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_189f0ea3bae4dd143d60d114fb060eca","title":"Agglomerative Approach","order":0},{"id":"seed_6a63babe42c42f6665c309fcc31ba64e","title":"Linkage Methods","order":1},{"id":"seed_4abf6067754d8c0fcadb906019fb9c1d","title":"Dendrogram Interpretation","order":2},{"id":"seed_a27cc935feaa648420227f59247cb744","title":"Cluster Extraction","order":3},{"id":"seed_29e5fee5d435cfa9c93f6e30017918e9","title":"Divisive Clustering","order":4}]},{"id":"seed_781388f8e96bee94ce065ff1db0c0064","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Spectral Clustering","order":5,"contentType":"LEARNING","contentSlug":"spectral-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ec02b9c7972c72124ee15be7ce7d8ad","title":"Graph-based Formulation","order":0},{"id":"seed_42ed0ee73260805bdbaf532293ad8d09","title":"Laplacian Matrices","order":1},{"id":"seed_9be9cf14470207f3f93da2acd71563ae","title":"Eigenstructure","order":2},{"id":"seed_298e69f87a81c3eaf9b76c67654e4574","title":"NCut Objective","order":3},{"id":"seed_a5e6b4e0cbe1f8d2eda0b37b1651c113","title":"Algorithm and Limitations","order":4}]},{"id":"seed_d758ff45163e4d63f66bd744695a183b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Evaluation","order":6,"contentType":"LEARNING","contentSlug":"clustering-evaluation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1dba3add7d890e7ed2fd9c537f280c45","title":"Internal Metrics: Silhouette and Calinski-Harabasz","order":0},{"id":"seed_c8b84931667d930903c024296f164e2a","title":"External Metrics: Adjusted Rand Index and Normalized Mutual Information","order":1},{"id":"seed_437524f646839aeb80265ae9fe9686e0","title":"Stability-Based Evaluation: Assessing Cluster Robustness","order":2},{"id":"seed_2e2e2d67723dec45f5a53a0d4d1e9be2","title":"Gap Statistic: Comparing Clusters to Null References","order":3},{"id":"seed_3cf56bbb1b170bcc21e7c1ea90f075d1","title":"Selecting the Number of Clusters: A Comprehensive Framework","order":4}]},{"id":"seed_14546b73b6439e5fd290e7eac815870c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":7,"contentType":"VISUALIZER","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_11791ef708ec1c800feac411b9252ab8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_68bdb028486489eb91f411607b9412b6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Density Estimation Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"density-estimation-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bbb5eca0fbcddc97a6984f2769ecc5c7","title":"Parametric vs Nonparametric Density Estimation","order":0},{"id":"seed_edd346fe05ab0177ed966a216f2fa23e","title":"Histogram Estimation","order":1},{"id":"seed_a82348a2647767f50e7ab3561b82c50b","title":"Kernel Density Estimation","order":2},{"id":"seed_60087af46b4e2f79831010f3bab28e1d","title":"Bandwidth Selection","order":3},{"id":"seed_78a5949540ebd7773826f892dcc520bb","title":"Multivariate Density Estimation","order":4}]},{"id":"seed_64066580a6eb406e58043cabb534f9da","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Kernel Density Estimation Theory","order":2,"contentType":"LEARNING","contentSlug":"kde-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_107fbc16a9928259ae66c5cb063a0707","title":"Kernel Functions","order":0},{"id":"seed_c3f4dad9ae7636c16b96c906a1ede04e","title":"Bias-Variance Tradeoff","order":1},{"id":"seed_c4447a6011b5b256bc1280309125ea13","title":"Bandwidth Selection Methods","order":2},{"id":"seed_c6395d360e39986339808328c2cdf376","title":"Curse of Dimensionality","order":3},{"id":"seed_089dbc641086db4da095e378a3d3de5e","title":"Variable Bandwidth","order":4}]},{"id":"seed_a4234b761eb1944905dac7fc3a64bf7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Gaussian Mixture Models","order":3,"contentType":"LEARNING","contentSlug":"gaussian-mixture-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6b3392112aba299b27c333d1879af50e","title":"Mixture Model Formulation","order":0},{"id":"seed_b159d44cf189a2d2f4c20208788cdac3","title":"Latent Variables","order":1},{"id":"seed_39f034a068e36dceed9c15c9c28abf63","title":"Marginal Likelihood","order":2},{"id":"seed_efc323d98975bf9a7ba16cc9928626f4","title":"Identifiability","order":3},{"id":"seed_cec33a366a8e47f2795f023698a401ce","title":"Model Selection","order":4}]},{"id":"seed_a2eacc9551297ba9d8a48167d5c81781","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"The Expectation-Maximization Algorithm","order":1,"contentType":"LEARNING","contentSlug":"expectation-maximization-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bbb1b3beed576c08ddbec3105e02b929","title":"EM for GMMs: Maximum Likelihood with Latent Variables","order":0},{"id":"seed_333a9128f075c97486db7491a457a1cb","title":"E-step and M-step: Complete Mathematical Derivation","order":1},{"id":"seed_dcb02b0cf3fae1925c138a93b3cc1c34","title":"Convergence Properties of the EM Algorithm","order":2},{"id":"seed_5d62c01c38b40befdb6cc73b6e44197b","title":"Initialization Strategies for EM","order":3},{"id":"seed_9dbbcb99ad730181df3b9b3f9918661a","title":"EM Variants: Extensions and Modifications","order":4}]},{"id":"seed_22d9cdb6f03f9f0e12d8f69cb35600cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Beyond Gaussian Mixtures","order":2,"contentType":"LEARNING","contentSlug":"beyond-gaussian-mixtures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b969dc87d61eabcadf2d1f183832fe54","title":"Student-t Mixtures","order":0},{"id":"seed_257fa56282ecc9a3b48787514804134e","title":"Mixture of Experts","order":1},{"id":"seed_144e11e2b74b3b8fa3281b06b9d1fe37","title":"Hidden Markov Models","order":2},{"id":"seed_b2d2d0fb3dbbb587e2f27c7739a3384a","title":"Infinite Mixtures (DPMM)","order":3},{"id":"seed_3858952829e79b826e732eac5ad03474","title":"Semi-Parametric Methods","order":4}]},{"id":"seed_8758cd49b4805ac155a60d016a0a2e38","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Density-Based Clustering","order":3,"contentType":"LEARNING","contentSlug":"density-based-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ba395c54f9d9ea8ca2eeefd4673a0028","title":"DBSCAN Algorithm","order":0},{"id":"seed_8fe88e5271030d69b551e64982149d60","title":"Core, Border, Noise Points","order":1},{"id":"seed_80be288ec193bdd9cc10f935b1c26dcb","title":"OPTICS Algorithm","order":2},{"id":"seed_f87a7db0e0102125b4b14e9a1f9d0a9b","title":"HDBSCAN","order":3},{"id":"seed_2e84f196bcb34ee0c34f3e7e2df72c0f","title":"Parameter Selection","order":4}]},{"id":"seed_983a6d13254cf67c792c3d012aa231b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Dimensionality Reduction Motivation","order":1,"contentType":"LEARNING","contentSlug":"dimensionality-reduction-motivation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_912e1c10fef59079b6b8269acf3230fd","title":"The Curse of Dimensionality","order":0},{"id":"seed_1ec58bb219d6e84a5e165a50d0124c3c","title":"Visualization","order":1},{"id":"seed_0686dd413b08ac697aeb85ae8cceedfb","title":"Noise Reduction","order":2},{"id":"seed_f2a25a2b1d5b5cec93bf07633721cbad","title":"Compression","order":3},{"id":"seed_15316592975c3f16e775c7ac595d9db8","title":"Feature Extraction vs Selection","order":4}]},{"id":"seed_af0b27842aa6a92042251980b9650ad8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Analysis Theory","order":2,"contentType":"LEARNING","contentSlug":"pca-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_20f7c3d891b0fc52c198c119ae67c687","title":"Maximum Variance Formulation","order":0},{"id":"seed_44e28c613c7e5f6528848566ac51a630","title":"Minimum Reconstruction Error","order":1},{"id":"seed_d1580eba7586e46c2cbd9b839cc6bee3","title":"Eigenvalue Problem","order":2},{"id":"seed_3aa8e145297a38c3e51fa7412fdc4e94","title":"Proportion of Variance Explained","order":3},{"id":"seed_081e9cb5217bdf74943b2959c53868ed","title":"Scree Plots","order":4}]},{"id":"seed_0ae7eac699c5b7e0ea73d5f865fb72b9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"PCA Variants","order":3,"contentType":"LEARNING","contentSlug":"pca-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ac9557094f991ff3a4042e12b6e0fada","title":"Standardized PCA","order":0},{"id":"seed_347ca9323de8f50d3285ede6621b14a3","title":"Weighted PCA","order":1},{"id":"seed_71b848987c7f843856ff7cd0f50d8974","title":"Robust PCA","order":2},{"id":"seed_d23b347e5521402c480e59649f653973","title":"Sparse PCA","order":3},{"id":"seed_0d0967c7364654ec49d6b109ad9b3467","title":"Incremental PCA","order":4}]},{"id":"seed_d09ca8e44e15e4da659e7b542cc04446","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Kernel PCA","order":4,"contentType":"LEARNING","contentSlug":"kernel-pca","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fd272f8d4db44a9ca2cbfc8360148ffb","title":"Nonlinear Dimensionality Reduction","order":0},{"id":"seed_5400ef79a7f070f169027bd79a365f88","title":"Kernel Trick for PCA","order":1},{"id":"seed_7f2388689534f45d7ba5f46954a27e6a","title":"Centering in Feature Space","order":2},{"id":"seed_9860cb79b866ce7f880c867613e8b08a","title":"Pre-image Problem","order":3},{"id":"seed_3300acc44a0cf2017421222e7c9d15ee","title":"Kernel Selection","order":4}]},{"id":"seed_85afe3c0f35cb0210def66038cb352a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Factor Analysis","order":5,"contentType":"LEARNING","contentSlug":"factor-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7bbdf7b48897b13c4fb8043fd4fa5f44","title":"Latent Factor Model","order":0},{"id":"seed_bce8abd653715f00413fe0662adac352","title":"Factor Analysis vs PCA","order":1},{"id":"seed_728f45bd9ac266cb008a76469ae821d1","title":"Factor Rotation","order":2},{"id":"seed_c8489c65a0f15ed44c442c3a39e43045","title":"Maximum Likelihood Estimation","order":3},{"id":"seed_0ef3d85aa0a970115ffe801fcc063ab5","title":"Interpretation","order":4}]},{"id":"seed_ab73773fa16c6ed214ba79504d07c444","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Independent Component Analysis","order":6,"contentType":"LEARNING","contentSlug":"independent-component-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c953d9d671e2834e5df5d31926fe26d","title":"The ICA Model","order":0},{"id":"seed_baf84ad7fa2165a7b18b802020a41511","title":"Non-Gaussianity","order":1},{"id":"seed_90cbf41d8f4a8c190e4f5568f9e72087","title":"FastICA Algorithm","order":2},{"id":"seed_211454791a8f15cc2083aba2251eb423","title":"Applications: Blind Source Separation","order":3},{"id":"seed_cfcbc153c5760cc4e28169716c306f99","title":"ICA vs PCA","order":4}]},{"id":"seed_c61ed49082d6e5a2589810425c9b31f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":7,"contentType":"VISUALIZER","contentSlug":"principal-component-extraction","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_01b5934d7f8ef0bf58ff57e63421b50f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":8,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_1c389cf030159b7da28f11969faaa3b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Manifold Hypothesis","order":1,"contentType":"LEARNING","contentSlug":"manifold-hypothesis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a46b438d566eb49621532fee820bad5b","title":"Manifold Definition","order":0},{"id":"seed_2c3f99ff1a35dde9b0c74c53a5742d29","title":"Intrinsic Dimensionality","order":1},{"id":"seed_0125cda1e7dda4017dc1215329c9bd74","title":"Manifold Examples","order":2},{"id":"seed_f0689c63aba5b0afa145a1be17182695","title":"Implications for Machine Learning","order":3},{"id":"seed_50733b5adaf78fbc0e9daabc9556c471","title":"Estimation Approaches","order":4}]},{"id":"seed_c1c7cd66ec0fed623f6f8f579b7062f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Multidimensional Scaling","order":2,"contentType":"LEARNING","contentSlug":"multidimensional-scaling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_53378b9ca349c331c069fc27ae7994eb","title":"Classical Multidimensional Scaling","order":0},{"id":"seed_aef6798750aad0d0539a4586f9896be5","title":"Metric Multidimensional Scaling","order":1},{"id":"seed_fe5447f3f28894b6b596ae9b4a795659","title":"Non-Metric Multidimensional Scaling","order":2},{"id":"seed_178344d97b978cffa4d596199c22c33d","title":"Stress Functions","order":3},{"id":"seed_c52fd77f8bf0aaeac21c2e1ddaf31605","title":"Limitations and Practical Considerations","order":4}]},{"id":"seed_71ecb495764198e1daba7a26711139fc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Isomap: Isometric Feature Mapping","order":3,"contentType":"LEARNING","contentSlug":"isomap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dfb6c8adf4eda3baa5ade9688ab42b1b","title":"Geodesic Distances: The Foundation of Isomap","order":0},{"id":"seed_12f15492860258793fd64c99e330f744","title":"Graph Construction: Building the Neighborhood Network","order":1},{"id":"seed_e870cfb1d05a8fcc008a1062641deca6","title":"Landmark Isomap: Scaling to Large Datasets","order":2},{"id":"seed_11b5778ab48d85fdf0607f9ca9b1d893","title":"Assumptions and Limitations: When Isomap Fails","order":3},{"id":"seed_3834689ea1d632e8a62d9657f4b0653e","title":"Isomap Applications: From Theory to Practice","order":4}]},{"id":"seed_73855f5207a19a961f18fcd49f63f1a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Locally Linear Embedding","order":4,"contentType":"LEARNING","contentSlug":"locally-linear-embedding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1fc4740d0c790d5908b98a8a73724f2c","title":"Local Linear Approximation","order":0},{"id":"seed_a6abe1791ca23c5526b2b051b0fdbdf1","title":"Weight Computation","order":1},{"id":"seed_21e47f52959ddab8535587dbe00ad204","title":"Embedding Optimization","order":2},{"id":"seed_1800071eccb52453aa7157de2bcbb523","title":"LLE Variants","order":3},{"id":"seed_73e541dd02b7a69a4a00e0c5bb0206ab","title":"Limitations","order":4}]},{"id":"seed_492a583a08bf6d335d7e2a481be94363","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"t-Distributed Stochastic Neighbor Embedding (t-SNE)","order":5,"contentType":"LEARNING","contentSlug":"t-sne","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_02ee1a862967d4ac401771d806d0929c","title":"The t-SNE Objective Function","order":0},{"id":"seed_3ac0c6acd4158516e2912980d02667bc","title":"The Perplexity Parameter","order":1},{"id":"seed_438161193705ce8795fee589e1bcee09","title":"Optimization Challenges in t-SNE","order":2},{"id":"seed_c724e618bd1c800c0d9eea020e962bae","title":"Interpretation Guidelines for t-SNE","order":3},{"id":"seed_c8e0477fe4e0e28cca9e31787a78a416","title":"Common Pitfalls in t-SNE Usage","order":4}]},{"id":"seed_605375d1d6334b96f0eead774cc314f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"UMAP (Uniform Manifold Approximation and Projection)","order":6,"contentType":"LEARNING","contentSlug":"umap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bb63f27779bef21990bf2ac13110b96e","title":"UMAP Theory","order":0},{"id":"seed_0091f8f64a0658497d67b968f6af6234","title":"Fuzzy Topological Representation","order":1},{"id":"seed_9bc5103a3f5ada83399a850f534b910b","title":"Cross-Entropy Optimization","order":2},{"id":"seed_05f04e22aa8a260bc0a7afbc62fc4859","title":"UMAP vs t-SNE","order":3},{"id":"seed_cd00d983d9ffd0ae94b467f53318e936","title":"Hyperparameter Tuning","order":4}]},{"id":"seed_bff5e7a0bc4fddf9a78ccd4982e6832d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Anomaly Detection Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"anomaly-detection-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a75f2514b43378710922e7e504567ab0","title":"Anomaly Types","order":0},{"id":"seed_37a380ea9282f31012aadbfe5badbfab","title":"Point vs Contextual vs Collective Anomalies","order":1},{"id":"seed_fb4533eb754db059404e5004dbe3e66d","title":"Supervised vs Unsupervised Anomaly Detection","order":2},{"id":"seed_f96a62d15c2a13293059eb100a888ca8","title":"Evaluation Challenges","order":3},{"id":"seed_158a60c6bf2c35aa5db6a14a6999f4a0","title":"Applications of Anomaly Detection","order":4}]},{"id":"seed_a2dd0d8bf5ebd6be590cd9d205ca3b5a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Distance-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"distance-based-methods","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4640896fdeb02b116edc122b4bcd5a4b","title":"KNN-Based Detection","order":0},{"id":"seed_0b909907eae74e35c96604ef032928b7","title":"Mean Distance to KNN","order":1},{"id":"seed_d92f381abd957a4a5e9d869239cf4e22","title":"Local Outlier Factor (LOF)","order":2},{"id":"seed_cfdf97f3c7eba77f317fa550721b480d","title":"LOCI Algorithm","order":3},{"id":"seed_abdaa7e995947a05d5e9b2a6b643e26d","title":"Curse of Dimensionality","order":4},{"id":"seed_547ff0a528fd9e11daeb22afe34ec216","title":"Parameter Selection","order":5}]},{"id":"seed_1a47f9a533ee25611d5c267af621d678","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Isolation Forest for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"isolation-forest","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_902eaf1bd0ed7c1e4a09f48c2bec1385","title":"Isolation Principle","order":0},{"id":"seed_f8ee0f72d0e08392e7f4beee0611fbca","title":"Random Partitioning","order":1},{"id":"seed_f5676bde047dc12d1068b5bf264107cf","title":"Path Length Scoring","order":2},{"id":"seed_6922d496d0b96961aea46aded9a46697","title":"Extended Isolation Forest","order":3},{"id":"seed_bfab2b5d79c8694a742e836a160225ca","title":"Parameter Selection","order":4}]},{"id":"seed_76b018ba7d1f8fc744e0978c016eb04e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Statistical Methods for Anomaly Detection","order":1,"contentType":"LEARNING","contentSlug":"statistical-methods-anomaly-detection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c9f85db4c96fc707e369f4d1f2755cb3","title":"Z-Score Method","order":0},{"id":"seed_6acc6263b1ae1202d4420689e8d6407b","title":"IQR Method","order":1},{"id":"seed_f1a4ca66ce7582c6c382b784e556e716","title":"Grubbs' Test","order":2},{"id":"seed_d7fdc195fa435af05d9d03c56e62464a","title":"Multivariate Methods","order":3},{"id":"seed_213b2c173b065677456f7f16df65678c","title":"Assumptions and Limitations","order":4}]},{"id":"seed_a8fc19bd78ba2746be4fd62a27ece203","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Density-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"density-based-methods-anomaly","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6b56e8b61131d3e8af54c2fcc7856cc4","title":"Relative Density","order":0},{"id":"seed_a9732a42f76251cab3d402316568935c","title":"DBSCAN for Outliers","order":1},{"id":"seed_96a7b0650b1eb4e7d19fce9166c6f670","title":"Local Density Estimation","order":2},{"id":"seed_b281a0f18493953e1ad791947c064d13","title":"Multivariate Density","order":3},{"id":"seed_205d0860a66609a6de9e79b132ac3d2f","title":"Scalability","order":4}]},{"id":"seed_4555f92571404920aa7a0ff10edd4942","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"One-Class Methods for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"one-class-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_33cd6a3e2cceee8f2f591bcb6eb54095","title":"One-Class SVM","order":0},{"id":"seed_b8078809723ff37f83ee93969145abd3","title":"Support Vector Data Description (SVDD)","order":1},{"id":"seed_0a7d5b9c468e68d8d129b6ad748de07e","title":"Autoencoders for Anomaly Detection","order":2},{"id":"seed_355ed88cbc012cdd90302a96ebb703f3","title":"Neural Network Approaches","order":3},{"id":"seed_bb883f6dcf92c98848973bff66f40afd","title":"Threshold Selection","order":4}]},{"id":"seed_ef80465133d1fe9ce84bf4ab98e19dea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"The Bayesian Framework","order":1,"contentType":"LEARNING","contentSlug":"bayesian-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_57ede80a0dfb4eed251ce8a2f694b3db","title":"Prior Beliefs","order":0},{"id":"seed_e66479a72e4cd01a68d6b738dedd9c04","title":"Likelihood Function","order":1},{"id":"seed_ddb54dd0f1518648084444acf584ffc8","title":"Posterior Distribution","order":2},{"id":"seed_f9332f3a80481143525c0f6cef36fc02","title":"Bayesian Updating","order":3},{"id":"seed_4c7ae9de131d8e972a26cba41a38bb32","title":"Point Estimates from Posterior","order":4}]},{"id":"seed_e20205c549adb1a21e00c02f2724d38b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Conjugate Priors","order":2,"contentType":"LEARNING","contentSlug":"conjugate-priors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c7ef3ffd1936a8a7a3bfba1bf8fba047","title":"Conjugacy Definition","order":0},{"id":"seed_9c253446fb8b056786d2a3ae3dd1f157","title":"Beta-Binomial","order":1},{"id":"seed_8fe1d086ccf4ea874c725c13bd41b140","title":"Gaussian-Gaussian","order":2},{"id":"seed_1687234d9f67927cc75c4d671ae819a1","title":"Dirichlet-Multinomial","order":3},{"id":"seed_a13f1a1e8dcf99041113a499b3abb3f6","title":"Practical Implications","order":4}]},{"id":"seed_b347cdec69ff8713db8903d02f3d40cd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Linear Regression","order":3,"contentType":"LEARNING","contentSlug":"bayesian-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c69b6ded351da5baea1b712c3acfc92a","title":"Prior on Weights","order":0},{"id":"seed_bc9c8107cfe67b4e064606e05c7bf126","title":"Posterior Derivation","order":1},{"id":"seed_bb66c141e2459b5b8c64af093e3c8a1b","title":"Predictive Distribution","order":2},{"id":"seed_0b31eaeaf054332560df761f0d2d5700","title":"Uncertainty Quantification","order":3},{"id":"seed_885657b641e84a3736c9940cd562b6a6","title":"Connection to Regularization","order":4}]},{"id":"seed_017d50d5c031b5e4d0e1445faf6ce982","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Model Comparison","order":4,"contentType":"LEARNING","contentSlug":"bayesian-model-comparison","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_22814dfeab80e59d04cc7c49d2e19dc1","title":"Marginal Likelihood: The Foundation of Bayesian Model Comparison","order":0},{"id":"seed_67d4acb9b96d5eac0f02ae5aa9b54f6b","title":"Bayes Factors: Quantifying Relative Model Evidence","order":1},{"id":"seed_08c71e635e6cd0a1352668348196e974","title":"Model Evidence: Understanding What Marginal Likelihood Measures","order":2},{"id":"seed_ed77ac4d24834fbfa5e84f9c8befc377","title":"Occam's Razor: The Bayesian Preference for Simplicity","order":3},{"id":"seed_110151d16421a7ad3179fe5adb3ff049","title":"Bayesian Model Averaging: Combining Models for Robust Predictions","order":4}]},{"id":"seed_d310f58ff165b49c4ec608aa0227cacf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Sampling Methods","order":5,"contentType":"LEARNING","contentSlug":"approximate-inference-sampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3f76f0d410a2c416feb5ad6f0dcce367","title":"Monte Carlo Methods","order":0},{"id":"seed_ddf30f65b6b041be44012fc07f4e9bc5","title":"Rejection Sampling","order":1},{"id":"seed_21c68122d9f12730912d7935be8dc47c","title":"Importance Sampling","order":2},{"id":"seed_44dc87aa918da2317593cfbe96d9a2db","title":"MCMC: Metropolis-Hastings Algorithm","order":3},{"id":"seed_4cfa9e6e917af2a36a3eb791c98af5c6","title":"Gibbs Sampling","order":4}]},{"id":"seed_5c847f1e908149fa7ac07f86188b319b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Deterministic Methods","order":6,"contentType":"LEARNING","contentSlug":"approximate-inference-deterministic","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4e4a1ef0d2f8f5b9aa35295f5758efde","title":"Laplace Approximation","order":0},{"id":"seed_43179d20e9494e54ad0651b10d8bb343","title":"Variational Inference Overview","order":1},{"id":"seed_ac9d0bd70cac5dfde182f6d56fc9b58d","title":"Expectation Propagation","order":2},{"id":"seed_b3330e7480d3df8908296d51e12bd38c","title":"Deterministic vs Stochastic Methods","order":3},{"id":"seed_76eca41d176450df29a491bc99075d8b","title":"Choosing Approximation Method","order":4}]},{"id":"seed_46d5fd4b152d1cd30b9443131d4b0c73","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Posterior Parameter Estimation with Conjugate Prior","order":7,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_ca2a8840c6015e1ebbb86e35dbba1513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Probabilistic Graphical Models","order":1,"contentType":"LEARNING","contentSlug":"probabilistic-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8950895aa4622ba05e81dfed077de872","title":"Probabilistic Graphical Models Overview","order":0},{"id":"seed_3094911232a94d940483af401f69d0f3","title":"Directed vs Undirected Graphical Models","order":1},{"id":"seed_e1fc5247b2aa5ed4a91f7284e86e0fb3","title":"Conditional Independence","order":2},{"id":"seed_c9b8d4aa1586eb9f9de47bfb5e9d196c","title":"D-Separation","order":3},{"id":"seed_5702d0208352b81b1733dfeb24b838bd","title":"Markov Blanket","order":4}]},{"id":"seed_f736480433bf6703491b1838f1c22be3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Bayesian Networks","order":2,"contentType":"LEARNING","contentSlug":"bayesian-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5eea4032f85cdfede42c406023f9aad9","title":"DAG Structure","order":0},{"id":"seed_3a5709b9eb7cd043da6d440a1855637b","title":"Factorization","order":1},{"id":"seed_3bd2448933a7395d9c28fbea1d71f008","title":"CPT Representation","order":2},{"id":"seed_b088713137555b3f7fc612a911cffa11","title":"Parameter Learning","order":3},{"id":"seed_f630a5895b3042d8b44098717328ce9c","title":"Structure Learning","order":4}]},{"id":"seed_29f3e6fc90cbda45c1970a76aa59d663","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Markov Random Fields","order":3,"contentType":"LEARNING","contentSlug":"markov-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_af9f586f674bf6e4ac05b4e35f0ce09f","title":"Undirected Graphs","order":0},{"id":"seed_384252b550fc16c66afbfb7afdaddfbb","title":"Potential Functions","order":1},{"id":"seed_e2ae54aafcf657fe7d743e42be5d3dc6","title":"Partition Function","order":2},{"id":"seed_076ec3dde0d456f5ad72ba406bf6849c","title":"Hammersley-Clifford Theorem","order":3},{"id":"seed_3158788ad60c0adbe748369c74a09cf2","title":"Energy-Based Models","order":4}]},{"id":"seed_24b1625575d59d42572164bfa2537fd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Inference in Graphical Models","order":4,"contentType":"LEARNING","contentSlug":"inference-in-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6aa0bef406691d15c92687d7462ed0e4","title":"Variable Elimination","order":0},{"id":"seed_4f6ac74ee592eafc6f39f3073f20b08d","title":"Belief Propagation","order":1},{"id":"seed_a747577f3daedb808971c7a19ba88dbe","title":"Junction Tree Algorithm","order":2},{"id":"seed_eeb4c61eb86343334c596641bc29fb95","title":"Loopy Belief Propagation","order":3},{"id":"seed_8efca05856e3bd5998dbff33a79e677f","title":"Approximate Inference","order":4}]},{"id":"seed_26906c87473209c81f340c709f82c90c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Hidden Markov Models","order":5,"contentType":"LEARNING","contentSlug":"hidden-markov-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_389a4f87ffb52df887b245e7dc3bfe4f","title":"HMM Structure","order":0},{"id":"seed_9f4eab4243846b1fe3d00bb2f5f53c51","title":"Forward Algorithm","order":1},{"id":"seed_67e0e1596a0b9aed578e7e3f111d2669","title":"Viterbi Algorithm","order":2},{"id":"seed_d7ff6f9584a023158a3644efe626b73d","title":"Baum-Welch (EM) Algorithm","order":3},{"id":"seed_9db56e30a3b5c9efc5f3c3b28583d98e","title":"Applications","order":4}]},{"id":"seed_70298c6e59e6d4aa717b493f4727004f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Conditional Random Fields","order":6,"contentType":"LEARNING","contentSlug":"conditional-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_542ce9a44ca588c80b65d760302448ca","title":"CRF Formulation","order":0},{"id":"seed_3dffcd1e45d6e120f972fadecbcf1218","title":"Linear-Chain CRF","order":1},{"id":"seed_1582f425ed12cdbea274ec383b79a394","title":"Feature Functions","order":2},{"id":"seed_efed3d7acbc46c26e3de085ddd9f09d6","title":"Training","order":3},{"id":"seed_645f81110acf1d13107c29254ad1a4fd","title":"Sequence Labeling","order":4}]},{"id":"seed_bfcd86cd712852ba44877065af04967f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"gp-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2b7c735cc967ead79dd6b7462d4d95bf","title":"Function Space View","order":0},{"id":"seed_0dabdf2e335aa43cea3e5d3a0c539a03","title":"Weight Space View","order":1},{"id":"seed_c81dc03d368309f71c673c0a5c454d22","title":"GP Definition","order":2},{"id":"seed_fba46d8f9480a1d84200d6c569b78810","title":"Mean and Covariance Functions","order":3},{"id":"seed_2b0dc76792bd09941081ab5b780d88eb","title":"Samples from GP Prior","order":4}]},{"id":"seed_379822a695955a78bd4ea16f38131ea2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Regression","order":2,"contentType":"LEARNING","contentSlug":"gp-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7f94f668ffa7e6ea7cbb42e020fe4458","title":"Posterior Derivation","order":0},{"id":"seed_618ac5edc2b9cfb835d2c73fbbde3a7f","title":"Predictive Distribution","order":1},{"id":"seed_be3486e2b14dad5fdff506115033b705","title":"Mean and Variance Prediction","order":2},{"id":"seed_8dd8643c7e33607673eafc995b0f6ba9","title":"Noise Handling","order":3},{"id":"seed_f3e2d25d5f577fdb91bb880fdfd252eb","title":"Closed-Form Solution","order":4}]},{"id":"seed_9c7afac43216ccc7ff3ab116639b4514","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Kernel Design for Gaussian Processes","order":3,"contentType":"LEARNING","contentSlug":"kernel-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e61c65e0ba4294b4b125adb630d8b9df","title":"Stationary Kernels","order":0},{"id":"seed_b555cf35adb8753ce3ebbb4db869d64c","title":"Non-Stationary Kernels","order":1},{"id":"seed_564223aa6c5ba9bdcd9b4c699aa19c97","title":"Kernel Composition","order":2},{"id":"seed_db6876abceff3ad783fbb93e398b07ac","title":"Spectral Analysis","order":3},{"id":"seed_e59ac9f2a51c8f742467bc6f05785053","title":"Automatic Relevance Determination","order":4}]},{"id":"seed_f84c8f27bdd419c923e2b1700bde6637","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Hyperparameter Learning in Gaussian Processes","order":4,"contentType":"LEARNING","contentSlug":"gp-hyperparameter-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_aa422e8aec77347db1c0897daac12e51","title":"Marginal Likelihood","order":0},{"id":"seed_c5e068969210af7349969b8ce951475d","title":"Gradient Computation","order":1},{"id":"seed_164ab4c219cc79549b51eac06dc68fc1","title":"Optimization Strategies","order":2},{"id":"seed_26eb57dc8bc1cc97d05f842fb702d735","title":"Cross-Validation Alternative","order":3},{"id":"seed_2063bb52833882fc3ff39324344fa26f","title":"Practical Considerations","order":4}]},{"id":"seed_6b381cf713c81aa18f4233121bab820e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Scalable Gaussian Processes","order":5,"contentType":"LEARNING","contentSlug":"scalable-gps","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ec82e73c14c1ad9e4ebf4dcfa4abd6c6","title":"Exact GP Limitations","order":0},{"id":"seed_b2c9d64c09f53f142dbed5b3cbdd66b8","title":"Sparse Gaussian Processes","order":1},{"id":"seed_6720222c64e5d753921e316c79603c3a","title":"Inducing Points","order":2},{"id":"seed_46cfabd97b873363e7f283b3ac1e2405","title":"Variational Sparse GPs","order":3},{"id":"seed_84407d4684fdbd4381594739e9c9e13f","title":"Random Features","order":4}]},{"id":"seed_f98eb34b8b26e060c9c2ee26e2db04e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Classification","order":6,"contentType":"LEARNING","contentSlug":"gp-classification","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7a23fd015cd725a41ea70c93459ada0e","title":"Non-Gaussian Likelihoods","order":0},{"id":"seed_3b23fca59688d6c2375b04e25d34ffa8","title":"Laplace Approximation","order":1},{"id":"seed_55576025f843c85f21cb77f0340088e4","title":"Expectation Propagation for GPC","order":2},{"id":"seed_5583abf2961ce51ff9e44abf45f4bba0","title":"Variational Inference for GP Classification","order":3},{"id":"seed_3fd06af349b053df235e0fbdedd15634","title":"Multi-class Extension","order":4}]},{"id":"seed_848b6363ea4fbdd529dd3bd0015d5968","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Inference Framework","order":1,"contentType":"LEARNING","contentSlug":"variational-inference-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b270dc6d384fec164c3a5ffa57936806","title":"Approximation Motivation","order":0},{"id":"seed_5334dc66eb008957959e9e1aca6201cb","title":"Variational Family","order":1},{"id":"seed_697baf93ff2994b235b0a7012d6ffaa8","title":"KL Divergence Objective","order":2},{"id":"seed_391889b768f9b3aa9a5b2c19e04d47cc","title":"ELBO Derivation","order":3},{"id":"seed_a6a679e3c00facd469f8fc28058d7622","title":"Optimization Perspective","order":4}]},{"id":"seed_347f0f34f42595c07ce415157b687b80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Mean-Field Approximation","order":2,"contentType":"LEARNING","contentSlug":"mean-field-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_35d87b9d17e7aa2baaa6dd2f76f79061","title":"Factorization Assumption","order":0},{"id":"seed_3caaf10a38f38a2c7243bd4a5cf93751","title":"Coordinate Ascent","order":1},{"id":"seed_e25c385c80d3de57c6f6cbee3625e672","title":"Update Equations","order":2},{"id":"seed_9cf1e718299d4bb42e0164e11f64a045","title":"Convergence","order":3},{"id":"seed_3908eda9710103514494268bd80d7613","title":"Limitations","order":4}]},{"id":"seed_e21603eecdb3d61c60b1ccc6bbea7857","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"ELBO Optimization","order":3,"contentType":"LEARNING","contentSlug":"elbo-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9d035bfefa27b6e6dd43a6ab6180d51b","title":"ELBO Decomposition","order":0},{"id":"seed_100e70a30f3e7feeaabb3b6cce07c042","title":"Reconstruction vs Regularization","order":1},{"id":"seed_ce7e67ef224ee89c4bad7609807fed6e","title":"Gradient Estimation","order":2},{"id":"seed_cf00017793e7e5a60ba7f877b199d54f","title":"Reparameterization Trick","order":3},{"id":"seed_7c3b2d2ffdf44ca6ad0f2d4dfbe5eff1","title":"Score Function Estimator","order":4}]},{"id":"seed_546b35e4882116c3d0a51680f2d835be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Stochastic Variational Inference","order":4,"contentType":"LEARNING","contentSlug":"stochastic-variational-inference","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fe2a182a076e39308d422e8a4ba94ff7","title":"Mini-Batch Optimization","order":0},{"id":"seed_0d03724f33b7d50b512e2cc5d8ef84dd","title":"Natural Gradients","order":1},{"id":"seed_12ae445dee97b248987053aeb333137f","title":"Scale to Large Datasets","order":2},{"id":"seed_3500ed004a93da01992853d1169686bd","title":"Convergence Analysis","order":3},{"id":"seed_c749740f3ff265cf601ab5aa8b1337fa","title":"Practical Considerations","order":4}]},{"id":"seed_1463afd50f358c4c3ce2e904178881a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Autoencoders","order":5,"contentType":"LEARNING","contentSlug":"ml-variational-inference-variational-autoencoders","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_02a73b52fc5f2fd4e84443e955de4f9d","title":"VAE Formulation","order":0},{"id":"seed_60c22382d04b7bdfa8e5071abdc4d7ef","title":"Encoder-Decoder Structure","order":1},{"id":"seed_04d8e09c2145402c2fcc906022518db9","title":"Reparameterization","order":2},{"id":"seed_36af5f01177448f5227301364e6a4a54","title":"Training Procedure","order":3},{"id":"seed_b651af28bc7bb8472a36798443e616f3","title":"Latent Representations","order":4}]},{"id":"seed_e9b79bdc20ae45b33f8b2f486ee6c680","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Advanced VI Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-vi-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9ef2119aac900ff46fbf91189ec0301e","title":"Normalizing Flows","order":0},{"id":"seed_8f9e86a4e4bbd545f3c0b1947b4c42ca","title":"Amortized Inference","order":1},{"id":"seed_2f4799e1474b7d7db97ebeb03ae60115","title":"Implicit Variational Inference","order":2},{"id":"seed_acad2dd93840c5c83d6252742db67b57","title":"Hierarchical Variational Inference","order":3},{"id":"seed_7497c9c5caf7d99d35c5ec41ed80e4ed","title":"Variational Inference for Deep Learning","order":4}]},{"id":"seed_f82ea691ae02c2164db9a4a902cfee61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_09795b81e9e1a2622018f5d8e70f2466","title":"Biological Neurons","order":0},{"id":"seed_e79fa17804429f447bfdfb27eceb0f48","title":"Artificial Neuron Model","order":1},{"id":"seed_e082c6ca8cca833d55b10990aef224b4","title":"Historical Development","order":2},{"id":"seed_1eac52ac8c54d7eaf42bca6a4cf7b1ba","title":"Perceptron Learning Rule","order":3},{"id":"seed_093f74f1ef7e8c78ba3881a14cdf2a7d","title":"XOR Problem","order":4}]},{"id":"seed_763ec03462b28e436c290ff5a6028cdd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Multi-Layer Perceptrons","order":2,"contentType":"LEARNING","contentSlug":"multi-layer-perceptrons","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0cd2f7c86006edf3eb8ac6ca09ba6f8c","title":"Network Architecture","order":0},{"id":"seed_4a63d2dcb5c0ee2f9070e4cc21d7ae46","title":"Hidden Layers","order":1},{"id":"seed_0ffe97f23e7ddcc3198df68d55085b9f","title":"Forward Propagation","order":2},{"id":"seed_fe4c92fae58a44647d55da9f9eb2e003","title":"Matrix Formulation","order":3},{"id":"seed_e3d4d5b90e54ba94bbca9f088829fb4e","title":"Activation Functions","order":4}]},{"id":"seed_a798e45c47f2ce91b6ef5d8994a2778d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Universal Approximation","order":3,"contentType":"LEARNING","contentSlug":"universal-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_85999d1d69cf025cbdfed8400dd52b14","title":"Universal Approximation Theorem","order":0},{"id":"seed_e5209a9aa7c7388a56e283dd309a8f3b","title":"Width vs Depth","order":1},{"id":"seed_865b26f5533a6942c17a0d2d85dea287","title":"Approximation Bounds","order":2},{"id":"seed_47ee926ff0a8002b5267c14d92a61d82","title":"Practical Implications","order":3},{"id":"seed_c4c530c165bec8374e8c11b3dcec8f9d","title":"Limitations","order":4}]},{"id":"seed_06736591bc987a4d2945f9be6f8633ce","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Functions","order":4,"contentType":"LEARNING","contentSlug":"activation-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_994f74896dda5de4b88fa18932dfd8c8","title":"Sigmoid and Tanh","order":0},{"id":"seed_babab3a6409bb5a0bcbf2b7a0499004e","title":"ReLU and Variants","order":1},{"id":"seed_fcbc5dde70e3f25ba4ac7a6168b06968","title":"Swish and GELU","order":2},{"id":"seed_d0013e053d9066c19ca036c1a0bb8450","title":"Softmax","order":3},{"id":"seed_1253ea8c8ea48b0d316c5ac37be8148d","title":"Selection Guidelines","order":4}]},{"id":"seed_ef1c8026b6efc42f60f2b27ef58f6ed5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Loss Functions","order":5,"contentType":"LEARNING","contentSlug":"loss-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ad8985203ebcb91702216a5dd51df523","title":"Cross-Entropy Loss: The Foundation of Classification","order":0},{"id":"seed_3b9abab61e06c35b811df0371f135d91","title":"Mean Squared Error: The Regression Workhorse","order":1},{"id":"seed_0ac456a1c487ceaf545ffde76cf4c02b","title":"Hinge Loss: The Max-Margin Perspective","order":2},{"id":"seed_63034c105be2ada50b5aab671c474f22","title":"Focal Loss: Conquering Class Imbalance","order":3},{"id":"seed_70fdc0b14d231418844e68c1758af1f2","title":"Custom Losses: Encoding Domain Knowledge","order":4}]},{"id":"seed_6cc64eb0e0ba1c8951cbe4fed8d38c06","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Output Layers","order":6,"contentType":"LEARNING","contentSlug":"output-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c2bf3107cb745f7d0c5d910e2b6ea99","title":"Regression Outputs","order":0},{"id":"seed_523d8f5cc9b36802d130e4ad94ed6ab1","title":"Binary Classification","order":1},{"id":"seed_810be67dce7c7d9da01411e514e65dca","title":"Multi-Class Classification","order":2},{"id":"seed_d8917b64dcb1b0d083d6f0aaf9e9c1a7","title":"Multi-Label Classification","order":3},{"id":"seed_d1ba82583dbdc5a2157771b0702ee982","title":"Structured Outputs","order":4}]},{"id":"seed_dd77b6b05260cf5c413474e5d189ece4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":7,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_0d1e9a5fd7d14cd9c1f49cb2ce5b143b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":8,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_81b4675ff4c64769f9e3ca3a0e12a3dc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Backpropagation Algorithm","order":1,"contentType":"LEARNING","contentSlug":"backpropagation-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_eff69e34e6a4d9a78415a3b73b7e1196","title":"Chain Rule Application","order":0},{"id":"seed_d959f945d8584814ef099673c86bba90","title":"Gradient Flow","order":1},{"id":"seed_ff74e2467f5c5f7f76cc7ae408bff851","title":"Jacobian-Vector Products","order":2},{"id":"seed_58cf9f9e52f36d66a2df339c4818e299","title":"Memory Considerations","order":3},{"id":"seed_66bd669e4e9fdd0bc2374a7e0a829658","title":"Gradient Checkpointing","order":4}]},{"id":"seed_6f6f054ba88424be3541f892015658c4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Gradient Descent Variants","order":2,"contentType":"LEARNING","contentSlug":"gradient-descent-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_31f04c4f020d5d11bfbd167825753708","title":"Batch Gradient Descent","order":0},{"id":"seed_71eb870ab243ce2db5396ccfd0ec9321","title":"Stochastic Gradient Descent","order":1},{"id":"seed_898b936ec29fe46dfd80f877f1d81783","title":"Mini-batch SGD","order":2},{"id":"seed_4e2a774e5f6bc6a8d37d51f134b3e0d1","title":"Learning Rate Selection","order":3},{"id":"seed_4d15965c24fcbe0048b6818491f8a714","title":"Convergence Analysis","order":4}]},{"id":"seed_0d04bb8be0289593e83fa5d0b2ad1b85","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Adaptive Optimizers","order":3,"contentType":"LEARNING","contentSlug":"adaptive-optimizers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cc76c352785b4f98854d5ba159f82af2","title":"Momentum","order":0},{"id":"seed_b8d533807eca035e291228e136e945be","title":"Nesterov Momentum","order":1},{"id":"seed_ddbe73d641a62006957d2bcabf12a848","title":"AdaGrad","order":2},{"id":"seed_66928aace0a0f6062202dfe580f522fa","title":"RMSprop","order":3},{"id":"seed_efe9e0e3ca3c1773cf07503283ce8375","title":"Adam and Variants","order":4}]},{"id":"seed_790d19157f66aa34b92db31dd4a5173b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Learning Rate Schedules: Orchestrating Optimization Dynamics","order":4,"contentType":"LEARNING","contentSlug":"learning-rate-schedules","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5d4c9f518ffedd49c8189e80ceb3efda","title":"Step Decay: The Foundation of Learning Rate Scheduling","order":0},{"id":"seed_43dd95a338d8fe04fad3a07b71cd7e1f","title":"Exponential Decay: Smooth and Continuous Learning Rate Reduction","order":1},{"id":"seed_4646f4dae6efa8af550485a31a0fd7e7","title":"Cosine Annealing: Elegant Schedules for Modern Deep Learning","order":2},{"id":"seed_22d272dfe8d8224f6c895c18b8a9cd62","title":"Warmup Strategies: Stabilizing Early Training Dynamics","order":3},{"id":"seed_b3f0e57b2eadf1fa9f6789beee9be3f0","title":"Cyclical Learning Rates: Oscillating for Better Exploration","order":4}]},{"id":"seed_62fc9f7677c99703f946f7592b4dd208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Computational Graphs","order":1,"contentType":"LEARNING","contentSlug":"computational-graphs","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_98a0cd5c2fdc648a230c9bb3a45f50ca","title":"Graph Representation","order":0},{"id":"seed_7442668ae46d23e093667429d61cdec4","title":"Forward Pass Computation","order":1},{"id":"seed_62df7feb434cda59e76ccf0b417cfacb","title":"Reverse Mode Autodiff","order":2},{"id":"seed_8f3ea27b8d6099d34c4f50185b683bc5","title":"Topological Ordering","order":3},{"id":"seed_8d00fe7b864b6495d2fd40c47e91a75d","title":"Modern Frameworks","order":4}]},{"id":"seed_b49f7466079a1b6662ac948c9e560dae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Second-Order Optimization Methods","order":2,"contentType":"LEARNING","contentSlug":"second-order-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_19e82105f99cb13615858bf9816c8969","title":"Newton's Method","order":0},{"id":"seed_6e60b0c142964bea06b9a810a1a23109","title":"Hessian-Free Optimization","order":1},{"id":"seed_ce0869517b6da49b3351c6b997085aa2","title":"Natural Gradient Descent","order":2},{"id":"seed_fd3dedebe037eae5b6f0845cc897ebb7","title":"K-FAC: Kronecker-Factored Approximate Curvature","order":3},{"id":"seed_3bf865167da3f29ca475e5725a9e1858","title":"Practical Limitations of Second-Order Methods","order":4}]},{"id":"seed_c856bc9ccfc814ca27e37c25317189eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":3,"contentType":"VISUALIZER","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_5e38cad8f738a9edc45cd7bb25048d92","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":4,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_79363c50d7cc2df59cf264620abf9d14","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Gradient Computation Engine (Basic Autograd)","order":5,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_1a37ecaa3606e5645687fde6f75686e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Capacity and Generalization","order":1,"contentType":"LEARNING","contentSlug":"capacity-and-generalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e831a2d571a02aed7b2594ebaaa04e99","title":"Model Capacity","order":0},{"id":"seed_33126647065e44994247dc961a5df5a9","title":"Effective Capacity","order":1},{"id":"seed_596efbdc5f4522ccc9f2b6cfcd3800da","title":"Double Descent","order":2},{"id":"seed_cf29ecc66b207003fd45ec5afd3fdf84","title":"Overparameterization","order":3},{"id":"seed_cba4da4251a70bba57f47ee09aaa1651","title":"Implicit Regularization","order":4}]},{"id":"seed_a4556ac961fca197a64db759fdfd37ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Weight Regularization","order":2,"contentType":"LEARNING","contentSlug":"weight-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_66cb62633e1dbeb921aa0beafbd0ddb9","title":"L2 Weight Decay","order":0},{"id":"seed_c6a37536dfbdd1d841d3eb06a3e8a3aa","title":"L1 Sparsity","order":1},{"id":"seed_14441f0dee1ac0d88cbc6e7ba7a0c8ee","title":"Weight Decay vs L2 Regularization","order":2},{"id":"seed_9adb308bfc2fd6ec34705d7dea8fdd8e","title":"Max-Norm Constraints","order":3},{"id":"seed_a062e3d2eb16f2b58cd538b077020618","title":"Spectral Normalization","order":4}]},{"id":"seed_28b239ae9dfb30b8bddecfbaa3928c28","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Dropout","order":3,"contentType":"LEARNING","contentSlug":"dropout","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_76e9d37c919e8e1fb632722025c61433","title":"Dropout Training","order":0},{"id":"seed_c5955f328d7cd9c3282aa35c5e14782b","title":"Inverted Dropout","order":1},{"id":"seed_ab0693d1e403a509572c9cd80c8732c3","title":"Dropout as Bayesian Inference","order":2},{"id":"seed_ede03534f09a000b408afe81a7343fcd","title":"Variational Dropout","order":3},{"id":"seed_41f81c48f9d5b04ed469d5b03be68d3a","title":"Dropping Other Things","order":4}]},{"id":"seed_e44f2f1dee2954db26113a899c23bb10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Batch Normalization","order":4,"contentType":"LEARNING","contentSlug":"batch-normalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0f8986233dc6e652d6eca1dcd5b6ec52","title":"Internal Covariate Shift","order":0},{"id":"seed_7c92b0ea3332c4e8d76f7a2232bf295c","title":"BatchNorm Formulation","order":1},{"id":"seed_4f6967b178b0b83fe6b8b09d9cece69d","title":"Training vs Inference","order":2},{"id":"seed_6a24beffebc8a6a833a86d42d5c48e17","title":"Layer Normalization","order":3},{"id":"seed_8df6bd4733f3d741b8f810f93f181077","title":"Other Normalizations","order":4}]},{"id":"seed_09158c8adb4d1dbd43a885617bdbd7b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Data Augmentation: Expanding Training Distributions","order":5,"contentType":"LEARNING","contentSlug":"data-augmentation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9ddecc4088469f1893d563abcbf646be","title":"Image Augmentations: Foundations and Techniques","order":0},{"id":"seed_7c1373a71f4635b1af840b6276322a4c","title":"Mixup and CutMix: Mixing-Based Data Augmentation","order":1},{"id":"seed_bc0beb3b967020a151e29fe32a862951","title":"AutoAugment: Learning Augmentation Policies","order":2},{"id":"seed_178c0b732dabc8ea880a6a57ec65d5c7","title":"Test-Time Augmentation: Inference-Time Enhancement","order":3},{"id":"seed_52b6ac25cb1fe968f5a96985d44f1c92","title":"Augmentation Strategies: Synthesis and Best Practices","order":4}]},{"id":"seed_020e4e8a002e13bfe417d1aac7d02803","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Implicit Regularization in Deep Learning","order":6,"contentType":"LEARNING","contentSlug":"implicit-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7ca88cad7675aa38243888ce35d52260","title":"SGD Implicit Bias","order":0},{"id":"seed_8a1707d36f31f6f2d2ed0a42e6afd9b6","title":"Architecture Choices as Implicit Regularization","order":1},{"id":"seed_540020b3f5a34922dc0e9ad8d984dfb3","title":"Early Stopping as Implicit Regularization","order":2},{"id":"seed_671bc5b4ce775b82a426490db3a2f30c","title":"The Lottery Ticket Hypothesis","order":3},{"id":"seed_8180037be2d488f14c415351d0d9a86e","title":"Benign Overfitting","order":4}]},{"id":"seed_6851d7814e8e4b64902dfba5a609d2de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bc5a5d3af4d576d5c5c39ac8cb35dcb3","title":"Discrete Convolution","order":0},{"id":"seed_b6eff19114cf6f6d52b3be16461b5ab8","title":"Cross-Correlation","order":1},{"id":"seed_f46a8e0250ea27f646f358b4ae454f0b","title":"Stride and Padding","order":2},{"id":"seed_d77029408295323d9361e684f4fe1806","title":"Dilation (Dilated/Atrous Convolutions)","order":3},{"id":"seed_d05d3969c9c32935845800f66f3a4e4c","title":"Implementation Details","order":4}]},{"id":"seed_94702d5823572341bdb7c6dac7a81e0d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Convolutional Layers","order":2,"contentType":"LEARNING","contentSlug":"convolutional-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_67f3b0d3bae43cf5233572b3987dfc42","title":"Parameter Sharing","order":0},{"id":"seed_03dab9165c54883bf956ff0be23415ef","title":"Translation Equivariance","order":1},{"id":"seed_b09f7fe0c91be0ae4c185878e55488de","title":"Receptive Field","order":2},{"id":"seed_c33c48b5de68ec688a993992134dafc1","title":"Feature Maps","order":3},{"id":"seed_8df1464c949a56d95c7f98233a4b3a85","title":"Multiple Channels","order":4}]},{"id":"seed_4af0cc0135bdb4df0247addfc29fe2ee","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Pooling and Downsampling","order":3,"contentType":"LEARNING","contentSlug":"pooling-and-downsampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_57f00e01aec45e3c71871485d6c8dcae","title":"Max Pooling","order":0},{"id":"seed_133eb9365db393949e0e4c7a58533ce5","title":"Average Pooling","order":1},{"id":"seed_97de879f0cbfb947126e698bd6a407a5","title":"Global Pooling","order":2},{"id":"seed_d93b1cff66a91a40266d0acc1a8e304b","title":"Strided Convolutions","order":3},{"id":"seed_b4181af4b65d6c7a25fe248ffbb8b4c6","title":"Pooling Alternatives","order":4}]},{"id":"seed_cd8e784eebe78b202a0a8c12e492d917","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"CNN Architectures: From LeNet to Modern Designs","order":4,"contentType":"LEARNING","contentSlug":"cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_da006f47f7b2e63e0094b8bcd50dc116","title":"LeNet: The Pioneer of Convolutional Neural Networks","order":0},{"id":"seed_15ac2d1e831e44637cbd4ddf2582d341","title":"AlexNet: The Deep Learning Revolution","order":1},{"id":"seed_b8c8db8c8f30842a71af5fe95722c186","title":"VGGNet: The Power of Depth and Simplicity","order":2},{"id":"seed_a85e83688612604dd5a028eb51a4d27b","title":"Inception/GoogLeNet: Going Deeper with Efficiency","order":3},{"id":"seed_fb4050bdef31b6610c595dd031364946","title":"CNN Design Principles: From Theory to Practice","order":4}]},{"id":"seed_3437e70f0dc0731e390eaacb69b9232e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Residual Networks and Skip Connections","order":5,"contentType":"LEARNING","contentSlug":"residual-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a64204b14a8b39b72ffcaf5b2588dbb6","title":"Skip Connections: Enabling Deep Network Training","order":0},{"id":"seed_c7765d84be586ac599eebd2915d3e754","title":"ResNet Architecture: Building Very Deep Networks","order":1},{"id":"seed_f3fff98dedd75800f6c8d58816462110","title":"Identity Mappings: Optimizing Information Flow","order":2},{"id":"seed_846e0425bf740ccce676ff8451997e2b","title":"DenseNet: Maximizing Feature Reuse","order":3},{"id":"seed_f5e566f040297bb7614357df26320063","title":"ResNeXt: Aggregated Residual Transformations","order":4}]},{"id":"seed_308ce9bd101b8dd160bbae4d0fb7454d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Modern CNN Architectures","order":6,"contentType":"LEARNING","contentSlug":"modern-cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6bb2b4bae93bbab9d03778f529b51523","title":"EfficientNet: Compound Scaling for Superior CNNs","order":0},{"id":"seed_1113f9c96c13516295f3abbfb48378ac","title":"MobileNet & ShuffleNet: Efficient Architectures for Edge Deployment","order":1},{"id":"seed_05de84b8d8fc43d6c09c2767b3e187da","title":"Neural Architecture Search: Automating Network Design","order":2},{"id":"seed_2bb4ecc047b309d59196f1e63f305543","title":"ConvNeXt: A ConvNet for the 2020s","order":3},{"id":"seed_a8b738aa653191747d48efd117acd8c5","title":"Vision Transformers Connection: CNNs Meet Attention","order":4}]},{"id":"seed_cbee09a0e607620cd98f7ddd2e9fa3d1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":7,"contentType":"VISUALIZER","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_110330320fbff6e7329228ba2e790938","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":8,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_5f1f573d9929ca2f3046a5cc8bcc8aca","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_235183e88e4f00a358e8d13db4003a6c","title":"Sequential Data Types","order":0},{"id":"seed_df45c797e69eac247606b74705241657","title":"Modeling Dependencies","order":1},{"id":"seed_be0d2845a5d96a8c226095478a442881","title":"Autoregressive Models","order":2},{"id":"seed_9ae638bfc09e87c05e1da688aa26f00c","title":"RNN Motivation","order":3},{"id":"seed_2e81eaf6aab701e54de2dd278acf8839","title":"Unfolded Computation","order":4}]},{"id":"seed_5db2b8211deeb14b772472b6b9cf59a0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Recurrent Neural Network Architecture","order":2,"contentType":"LEARNING","contentSlug":"rnn-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ffb4c14fff3dfbc033bd73f8472d88b","title":"Hidden State Dynamics","order":0},{"id":"seed_70f8fd4429bef62d71f966e83651d04f","title":"Parameter Sharing in Time","order":1},{"id":"seed_c8e71e12f1e158389e95fedd69b73949","title":"Forward Computation","order":2},{"id":"seed_b6ea07778fb37bcf3351d71ea728f91e","title":"Backpropagation Through Time","order":3},{"id":"seed_5c16d9657942ccab97b5ff1e49bf5a96","title":"Computational Considerations","order":4}]},{"id":"seed_162d876b2b2a4f4479fbf1d37a5e4a64","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Vanishing and Exploding Gradients","order":3,"contentType":"LEARNING","contentSlug":"vanishing-exploding-gradients","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_91a92d15458eda0c1c88fdecbbc485de","title":"Gradient Flow Analysis","order":0},{"id":"seed_1ae50a5a3081a2de8f5f35f809d81393","title":"The Vanishing Gradient Problem","order":1},{"id":"seed_50a867c556aa8b4814cb6caad5c791f2","title":"The Exploding Gradient Problem","order":2},{"id":"seed_b400e5dcb221744be78b18270112d079","title":"Gradient Clipping","order":3},{"id":"seed_0c2da4274f085a450a4df0e1aee3117f","title":"Better Architectures","order":4}]},{"id":"seed_3820671dcdd4232fc4cdd4d868cea6d2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Long Short-Term Memory","order":4,"contentType":"LEARNING","contentSlug":"long-short-term-memory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fb984fd798419db31c63f8a5cecbbbb8","title":"LSTM Cell Architecture","order":0},{"id":"seed_7ecb28a76098a5c89ff6e72cf3e7d570","title":"Gate Mechanisms","order":1},{"id":"seed_328316103f8585f2c20c6742f237e579","title":"Cell State Highway","order":2},{"id":"seed_fce6c698887b2b2d151159db90ed96a5","title":"Gradient Flow Improvement","order":3},{"id":"seed_c5e35e5a2ea9d486eb547193536f2ece","title":"LSTM Variants","order":4}]},{"id":"seed_fa7c5556299f5f97c15a403a7b11cb8c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Gated Recurrent Units","order":5,"contentType":"LEARNING","contentSlug":"gated-recurrent-units","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e4c40359099fa0097e10411d1bd0efa7","title":"GRU Simplification","order":0},{"id":"seed_21b2b127f5b79920ff2684572b60a73c","title":"Update and Reset Gates","order":1},{"id":"seed_1dbe208580244da5aecff1214c7dcfc4","title":"GRU vs LSTM","order":2},{"id":"seed_7b581d024af0838f906d528de70243a0","title":"Empirical Comparison","order":3},{"id":"seed_ccaa48691d6129006a9ff25747a9e59b","title":"When to Use Which","order":4}]},{"id":"seed_37f61dfc75c977d4f49c5d259f50879f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Advanced RNN Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-rnn-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_409b2dcb729d9cace5f00fdde20d22ba","title":"Bidirectional RNNs","order":0},{"id":"seed_3b30e362f910c52ac1e0e6992fe8153e","title":"Deep RNNs","order":1},{"id":"seed_c8248c383b61031977ab62a8dcbc7394","title":"Sequence-to-Sequence","order":2},{"id":"seed_830013df4edc054f523accca501c138c","title":"Encoder-Decoder","order":3},{"id":"seed_c3d21849ccc390fd5ca72f4f4380ac58","title":"Attention Preview","order":4}]},{"id":"seed_78f2403d64ba60eab0edef165e79785d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":7,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_e56a4e18774f4156d0a77d0180048778","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":8,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6a5518e35a9be244d03d87d3d56f50e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b14bf2d6fac6f8043c7a3ceccebb10ee","title":"Attention Intuition","order":0},{"id":"seed_b8198f9c2f13b55af2d6cc72c70da1fd","title":"Query-Key-Value Framework","order":1},{"id":"seed_bfb6f6741e37512a120b3c81aabff1e7","title":"Soft Attention","order":2},{"id":"seed_ee5ab2b20568fb8d3bf7f324302fd0fb","title":"Hard Attention","order":3},{"id":"seed_45e607b88ddea1964fe1ff474f6db67e","title":"Attention Visualization","order":4}]},{"id":"seed_e9ba71f8e6d0d3c6af728ccfb81965d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention","order":2,"contentType":"LEARNING","contentSlug":"self-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_51168ac1cf7f9f28ef6fad645e00e895","title":"Self-Attention Formulation","order":0},{"id":"seed_7bfaa9e65ec2d6d85ac929e7ad8c3254","title":"Computing Attention Weights","order":1},{"id":"seed_8b3c42e8f86b6600e5a13fe1a2d91bff","title":"Scaled Dot-Product Attention","order":2},{"id":"seed_0a3588b63f585e220f05b29da89fdd50","title":"Position Independence","order":3},{"id":"seed_34d29cb9d119c91773c517a8f7fa4ed7","title":"Complexity Analysis","order":4}]},{"id":"seed_2e1937ca8c0da75933b343ae37dc2cd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Multi-Head Attention","order":3,"contentType":"LEARNING","contentSlug":"multi-head-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c1e2d8137df1cedb4585287f81565d1","title":"Multiple Attention Heads","order":0},{"id":"seed_3d322f9a99be5576157c6859a91876e0","title":"Head Concatenation","order":1},{"id":"seed_0b7fdd66835d7aedb0877a1cd7cb8a6e","title":"Parallel Computation","order":2},{"id":"seed_6d9c34e0301b0e62d06abbcc64449582","title":"What Heads Learn","order":3},{"id":"seed_73dd50a18ebfe29f19b1ff4c071b2ebb","title":"Head Pruning","order":4}]},{"id":"seed_042e91151586cd6a2bb93b767eee318d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Architecture","order":4,"contentType":"LEARNING","contentSlug":"transformer-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c01e6c661546f21ccdc58bdf4005717b","title":"Encoder-Decoder Structure","order":0},{"id":"seed_5e626cab9eeed77508a08fc546c5ca91","title":"Layer Normalization","order":1},{"id":"seed_703f97d66ba79df132f49ae96d5f63f6","title":"Feed-Forward Layers","order":2},{"id":"seed_de0cd90d14bfb5700dbc1bb980e91444","title":"Residual Connections","order":3},{"id":"seed_db03a67fd0c8d2dbfa0037454a49e568","title":"Full Architecture","order":4}]},{"id":"seed_79661700b7c47fae120d0bd30aa077f8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Variants","order":5,"contentType":"LEARNING","contentSlug":"transformer-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2c4d740414ff0c3525de6d1c3ac4f66f","title":"BERT: Bidirectional Encoder Representations from Transformers","order":0},{"id":"seed_246305c23c09675a6c8b4e49d98cec2b","title":"GPT: Generative Pre-trained Transformer","order":1},{"id":"seed_ae0672cd732ecc5cff8ede5bb4829e7d","title":"T5: Text-to-Text Transfer Transformer","order":2},{"id":"seed_7c44fa4d4d359f2bb346c2f3eeebd71f","title":"Efficient Transformers: Overcoming the Quadratic Bottleneck","order":3},{"id":"seed_9bbffc5fedc1fc914aa2a8cc10f17fc3","title":"Vision Transformers: Transformers Meet Computer Vision","order":4}]},{"id":"seed_2c16c77d2922828776983c6932e17364","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":6,"contentType":"VISUALIZER","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6ea15e630262800d691de2dbdb9277ad","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":7,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_ea4826b8cb5aa5c6c91df6bbd5b3562f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention Transformers","sectionOrder":47,"title":"Positional Encoding","order":1,"contentType":"LEARNING","contentSlug":"positional-encoding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_21382e3099cbb582b52b8a1184c2e406","title":"Position Importance","order":0},{"id":"seed_0c95bea8a5315a2b15d24440278caad9","title":"Sinusoidal Encoding","order":1},{"id":"seed_58390aa6bf0ebac42bb5856dc84e1c13","title":"Learned Positional Embeddings","order":2},{"id":"seed_ea0113872b05c0dd5eebd4513a402d38","title":"Relative Positional Representations","order":3},{"id":"seed_da4b02358ea0119f86c27c10168d708e","title":"Rotary Position Embeddings (RoPE)","order":4}]},{"id":"seed_8a58a3ebe0e519a060e593bb2f787e77","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_49b537de6e9908485eaad849bb0bcf7e","title":"Generative vs Discriminative","order":0},{"id":"seed_408ad22b4a2a0bf8eb924405fe68f7c8","title":"Density Estimation","order":1},{"id":"seed_65afd7be1fa1b93b9ba03547bc72070f","title":"Sampling","order":2},{"id":"seed_9cb97bc76756a5a731ce25ed5823c230","title":"Latent Variables","order":3},{"id":"seed_bc25a1164cc3bc0ecd0912bbf1222d28","title":"Evaluation Challenges","order":4}]},{"id":"seed_04aa20659bf19165d9aef4e2b23a6ff7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Adversarial Networks","order":2,"contentType":"LEARNING","contentSlug":"generative-adversarial-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_92cde40b3bdeccae82ecf4550a808c4e","title":"GAN Framework","order":0},{"id":"seed_acfa8120ad108063d20858f36f62a7fc","title":"Generator and Discriminator","order":1},{"id":"seed_d553a28de7ee150938984a8729e2bb57","title":"Minimax Objective","order":2},{"id":"seed_a389135607c562d3b5d855582661f7ae","title":"Training Dynamics","order":3},{"id":"seed_f3b99f7f9d04f1679b474545aa941b7c","title":"Mode Collapse","order":4}]},{"id":"seed_fbcfefe6c406993e3296659d69308542","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"GAN Variants","order":3,"contentType":"LEARNING","contentSlug":"gan-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_11d9ac658b53d6c176c460daeea9d74e","title":"DCGAN: Deep Convolutional Generative Adversarial Networks","order":0},{"id":"seed_769d897f8f96479073ff98a359650cf0","title":"Wasserstein GAN: A New Paradigm in GAN Training","order":1},{"id":"seed_ff0cf933079c08ece6b6faf036e0c068","title":"Progressive GAN: Growing GANs From Low to High Resolution","order":2},{"id":"seed_5e24cbada0120706452c4825fcc79bbf","title":"StyleGAN: Style-Based Generation with Disentangled Latent Spaces","order":3},{"id":"seed_3641ef9444486545ccc912a0c81da198","title":"Conditional GANs: Controlled Generation Through Conditioning","order":4}]},{"id":"seed_b7c820446f87f68f23f8ec05b9981baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Flow-Based Models","order":4,"contentType":"LEARNING","contentSlug":"flow-based-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_84905931e293f2b831882abf16d6df8f","title":"Normalizing Flows: Foundations and Principles","order":0},{"id":"seed_0cb2069db39199813bba39c7f9c750cb","title":"Change of Variables: The Mathematical Heart of Flows","order":1},{"id":"seed_ffeba17620712c8ab9ba1c17ed0aefd6","title":"Coupling Layers: The Architectural Breakthrough","order":2},{"id":"seed_a49ca4463ab83e030fce5eb9bbd9b015","title":"RealNVP and Glow: State-of-the-Art Image Flows","order":3},{"id":"seed_b306fff82bd6bbca0f5f48590a4700fd","title":"Continuous Normalizing Flows: Neural ODEs","order":4}]},{"id":"seed_ec6ae3647c56081215e3cbf9ded2e8dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Diffusion Models","order":5,"contentType":"LEARNING","contentSlug":"diffusion-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7acdba52274ee2acd853cc957f07dc38","title":"The Diffusion Process","order":0},{"id":"seed_212a0dbff143bd8449367dfce583c578","title":"Denoising Score Matching","order":1},{"id":"seed_1a240b17a7991aa7338d80747ee8e174","title":"Denoising Diffusion Probabilistic Models (DDPM)","order":2},{"id":"seed_a1d1876bafaa3bd54199fb1f1e308078","title":"Score-Based Generative Models","order":3},{"id":"seed_b02b79f05d5bc6fcd395a71923c782c6","title":"State of the Art in Diffusion Models","order":4}]},{"id":"seed_904927660fd8d6c41d1aba5f40bf3672","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised And Self Supervised Learning","sectionOrder":49,"title":"The Label Scarcity Problem","order":1,"contentType":"LEARNING","contentSlug":"the-label-scarcity-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b94d1c67041286d072d179c5c6680f87","title":"Labeling Costs: The Economics of Data Annotation","order":0},{"id":"seed_59ce4cb0d7c7fdd66dbd495cebeb3586","title":"The Semi-Supervised Setting: Formal Framework","order":1},{"id":"seed_be3aa6079e6b2923cf296d7b7f7da4dd","title":"Transductive vs Inductive: Two Philosophies of Learning","order":2},{"id":"seed_89de03bd0266972e1cf8907b70e0e46b","title":"Assumptions: The Foundations of Semi-Supervised Learning","order":3},{"id":"seed_e60fec48c39ee91d8a9efcdff1c87f0a","title":"Evaluation Challenges: Measuring SSL Effectiveness","order":4}]},{"id":"seed_e4208222d89c41fea5968e9e5f3c53d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Semi-Supervised Methods","order":1,"contentType":"LEARNING","contentSlug":"semi-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7273b271be996b7f47f56f5a2a8009be","title":"Self-Training","order":0},{"id":"seed_9492bec0dac87a9b35e7dbef9e06b787","title":"Co-Training","order":1},{"id":"seed_6096f8363aa5b263799902c3e60e4a4f","title":"Label Propagation","order":2},{"id":"seed_02f188120173e71fa394a1d94886d485","title":"Graph-Based Methods","order":3},{"id":"seed_3a665d07f5580d4735619d1924070d9a","title":"Generative Approaches","order":4}]},{"id":"seed_3bff5f8dfe463704b7b4b20e9fa5d91c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Consistency Regularization","order":2,"contentType":"LEARNING","contentSlug":"consistency-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3de7771529a2843792cfc6145fa48711","title":"Consistency Assumption","order":0},{"id":"seed_752ca380224c5c691981e62ce2f704d4","title":"Data Augmentation Consistency","order":1},{"id":"seed_cb7701ec811e773e80576da4fa007e92","title":"UDA and FixMatch","order":2},{"id":"seed_80b17f6390eea8a31dda4eb34b2e6f5b","title":"MixMatch","order":3},{"id":"seed_e4943630cb32e04202c86da79bb64ad1","title":"Pseudo-labeling","order":4}]},{"id":"seed_cef5c1d33e1344056ba5a145591e865b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Self-Supervised Learning","order":3,"contentType":"LEARNING","contentSlug":"self-supervised-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2883594f2d194893b21116db5b10b611","title":"Pretext Tasks","order":0},{"id":"seed_c7429f61bf2faf51351681a835989714","title":"Learning Representations","order":1},{"id":"seed_82c9830dab7e86824ad66c59829aebaa","title":"Contrastive Learning","order":2},{"id":"seed_d7c981901ff8898ddac0987cd67e47da","title":"Non-Contrastive Methods","order":3},{"id":"seed_02264a069440669db434d7a381b56000","title":"Evaluation Protocols","order":4}]},{"id":"seed_28431ac2324f528d7232189a00afdae7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Contrastive Learning","order":4,"contentType":"LEARNING","contentSlug":"contrastive-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_805c0204c5ece435791de4ad03a6bfcf","title":"InfoNCE Loss","order":0},{"id":"seed_e3f13263c9c801fdb5a880285402b803","title":"SimCLR: A Simple Framework for Contrastive Learning","order":1},{"id":"seed_600178cb87e607adafdc78ee3ec01010","title":"MoCo: Momentum Contrast for Unsupervised Representation Learning","order":2},{"id":"seed_34be5e97413d532e59d0c1cc1a3c99fe","title":"Positive and Negative Pairs: The Foundation of Contrastive Learning","order":3},{"id":"seed_fe55c5b1ec3ad1990fdb4b64a5b0e900","title":"Data Augmentation: The Cornerstone of Contrastive Learning","order":4}]},{"id":"seed_89bfe981eb096f5677324a5cb95c5c3f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Modern Self-Supervised Methods","order":5,"contentType":"LEARNING","contentSlug":"modern-self-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0668b2b0ab3ceaeafdc7fb1e75d80b4b","title":"BYOL and SimSiam: Learning Without Negative Samples","order":0},{"id":"seed_eedc9472f765deb20e291e6c57703484","title":"Clustering-based Self-Supervised Learning","order":1},{"id":"seed_ae7996966a89fd0ac56c5c45aaa8bf6b","title":"Masked Modeling: Learning by Reconstruction","order":2},{"id":"seed_87b25599782952bbe49daa3aa37eb5af","title":"Vision-Language Pre-training: Bridging Modalities","order":3},{"id":"seed_72dda2305547175d966282b77f3dc5a6","title":"Foundation Model Training: Scaling Self-Supervision","order":4}]},{"id":"seed_48b71cced9af84fce00dd5f87802a040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning And Domain Adaptation","sectionOrder":51,"title":"Multi-Task Learning","order":1,"contentType":"LEARNING","contentSlug":"multi-task-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ffcb8c9c63efc53b2da00340790dd0e4","title":"Shared Representations","order":0},{"id":"seed_60ca375242ca15c8967bd77412d42ea5","title":"Hard vs Soft Parameter Sharing","order":1},{"id":"seed_beb212c257539a3a79dd7b96fe6a57c4","title":"Task Relationships","order":2},{"id":"seed_2c296bf58f61996727d0f0125ecdafd2","title":"Optimization Challenges","order":3},{"id":"seed_4bbd04a744d4c14f400e7dbe966a3df8","title":"When MTL Helps","order":4}]},{"id":"seed_bad0d98a933a15ea25cabed00cac2414","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Transfer Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"transfer-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4c49302f1e96deff0142c99ba9b7fdc1","title":"Transfer Learning Definition","order":0},{"id":"seed_6281b069bdc73cdb2ba68151fc3cb65a","title":"Source and Target Domains","order":1},{"id":"seed_dbd161b285a64535a5650647505be45a","title":"When Transfer Helps","order":2},{"id":"seed_fb89cf127cd6ac11ad5e5208d4452897","title":"Negative Transfer","order":3},{"id":"seed_ba30e3f2f613d3b1ac9823722506a7eb","title":"Transfer Learning Taxonomy","order":4}]},{"id":"seed_18d69040c7cd9a295f5552d746a8012b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Feature-Based Transfer","order":2,"contentType":"LEARNING","contentSlug":"feature-based-transfer","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c4720f0f0ef403744cfed039ebb2db0","title":"Pre-trained Representations","order":0},{"id":"seed_b9059313044a9abf54782ad529f442aa","title":"Frozen Features","order":1},{"id":"seed_2115de34d28d39763ec65fc14549a400","title":"Feature Extraction","order":2},{"id":"seed_47f72706dd3b6337fdbe778f234c72a2","title":"Domain Discrepancy","order":3},{"id":"seed_d02d0b44f60101f10247db2ea5b1fa98","title":"Feature Adaptation","order":4}]},{"id":"seed_8e9f01590c85745935d1f7e223be6927","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Fine-Tuning Strategies","order":3,"contentType":"LEARNING","contentSlug":"fine-tuning-strategies","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cb2ca8b4e06104ef5ec44d8b9d921f09","title":"Full Fine-Tuning","order":0},{"id":"seed_c9e4af4829a2d491ea8ad369c71be1df","title":"Selective Fine-Tuning","order":1},{"id":"seed_f203cc52c5eac01973d72ea7a8335c95","title":"Learning Rate Strategies","order":2},{"id":"seed_7717bb86c9fcf13b86cb335ed5d776aa","title":"Catastrophic Forgetting","order":3},{"id":"seed_14fde34458578244ae6d9c6f1e70d993","title":"Regularization Approaches","order":4}]},{"id":"seed_4c02fca3ba18a5932d556e4a7780eeeb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Domain Adaptation","order":4,"contentType":"LEARNING","contentSlug":"domain-adaptation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dec6237b8f7cf4ea1ed097071fa796ed","title":"Domain Shift Problem","order":0},{"id":"seed_bdaf0e1a6513a4cd10801361c0f91add","title":"Covariate Shift","order":1},{"id":"seed_8cb79ad18b259180186dba4092c6982a","title":"Distribution Matching","order":2},{"id":"seed_d267b53ccaae39cfbfa6ee1dd98a10f9","title":"Adversarial Domain Adaptation","order":3},{"id":"seed_8dc66df94d35b46b5c94bf32791d5295","title":"Domain Generalization","order":4}]},{"id":"seed_3d3815ce091181ea37c5c2491dc123c9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Meta-Learning: Learning to Learn","order":5,"contentType":"LEARNING","contentSlug":"meta-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ddf3a062957b6b92f302f99a926bd13","title":"Learn to Learn: The Meta-Learning Paradigm","order":0},{"id":"seed_aae4cc1587fe89b803a9e09d34449c5a","title":"Few-Shot Learning: Mastering Minimal Data","order":1},{"id":"seed_75407bcceb85790bfe6832dd1e8eb1a6","title":"Model-Agnostic Meta-Learning (MAML)","order":2},{"id":"seed_9d8f05b14949c177bc456211ddacfad2","title":"Prototypical Networks: Learning to Compare","order":3},{"id":"seed_3a326b65502383487e2ff533c888b48f","title":"Meta-Learning Applications: From Vision to Language to Robotics","order":4}]},{"id":"seed_146bb4046d37075972f5f1d4a35d8133","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a1440050fc1a0eb133c023553c92cd0c","title":"Agent-Environment Interaction","order":0},{"id":"seed_da02dc3ca4fe119dc66e4c9b2e0482d9","title":"States, Actions, Rewards","order":1},{"id":"seed_d63fae8c38ef49a11ac345ecf4e39919","title":"Policies","order":2},{"id":"seed_d8a1367304379167e8f13f653e6df947","title":"Value Functions","order":3},{"id":"seed_93008a2220640a580ee8a0c4f3371e0c","title":"Markov Property","order":4}]},{"id":"seed_4a14d44ad08d752faaf01a1fe34edd4b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Markov Decision Processes","order":2,"contentType":"LEARNING","contentSlug":"markov-decision-processes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_baad5061f468865e0971a0ec531b56fc","title":"MDP Formulation","order":0},{"id":"seed_105b4da2a7df57cb514aaecd9d49f025","title":"State Transitions","order":1},{"id":"seed_45cdf65b967fd294b81f007835337ee3","title":"Reward Signals","order":2},{"id":"seed_04d8628cfd9f5cfa7043d96e60d0572f","title":"Discount Factor","order":3},{"id":"seed_f5dc224b84809ea9eadbede9875a855e","title":"Bellman Equations","order":4}]},{"id":"seed_e962e18f072550c1bddf2875fc11d4d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Value-Based Methods","order":3,"contentType":"LEARNING","contentSlug":"value-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a44800bd57e063c3c12bd7e79070846a","title":"Value Iteration","order":0},{"id":"seed_685fc314c322a031b19ad648f93d3075","title":"Q-Learning","order":1},{"id":"seed_4d933693440da6c2e6f3330f54017806","title":"SARSA","order":2},{"id":"seed_424dd68e1f950f15aa7d6c32f26c8d11","title":"Temporal Difference Learning","order":3},{"id":"seed_c6be6c35bde2b317180927321e534a83","title":"Eligibility Traces","order":4}]},{"id":"seed_8dafdc1a94769c8c45e42c0a06ed363b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Policy-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"policy-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_01ced883ed583aaa3a6c3ff114050637","title":"Policy Gradient Theorem","order":0},{"id":"seed_321db62cc5d4859530196072c241f8d3","title":"REINFORCE Algorithm","order":1},{"id":"seed_97ca06768ff43841cab3a97d2c0010e2","title":"Variance Reduction","order":2},{"id":"seed_2d727389d38bbaa56cdf4795491da792","title":"Actor-Critic Methods","order":3},{"id":"seed_e0a16febcec9ed7a7011fc3c799e57b9","title":"Advantage Functions","order":4}]},{"id":"seed_306e6c5664e5b5241b8ca0658b77ed61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Deep Reinforcement Learning","order":5,"contentType":"LEARNING","contentSlug":"deep-reinforcement-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_85dabf15f96af8b65718437828eaef57","title":"DQN Architecture","order":0},{"id":"seed_0e902fe19447c2a8731f33a83425dded","title":"Experience Replay","order":1},{"id":"seed_bdf4a231c531d0aae0a56d3fcdd19a81","title":"Target Networks","order":2},{"id":"seed_94f6aeeff0d2b84b2dad53a4a763133d","title":"Rainbow DQN","order":3},{"id":"seed_286d78957c58bfa73e325fa572f98d27","title":"PPO and SAC","order":4}]},{"id":"seed_83233c3c4eba3d8393e4b5df2a901c2f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Applications and Challenges","order":6,"contentType":"LEARNING","contentSlug":"rl-applications-and-challenges","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c91f7b85dbf8fb68a27f7983fd635a77","title":"Game Playing: RL's Proving Ground","order":0},{"id":"seed_6303b2baf45452eecabed6200b85c5b8","title":"Robotics: RL Meets the Physical World","order":1},{"id":"seed_adf80c63030036e2db509956663d62a9","title":"Sample Efficiency: Learning from Limited Experience","order":2},{"id":"seed_9b6b38c518d11a6aeffce2ed0ffd26bf","title":"Exploration: Discovering Rewarding Behaviors","order":3},{"id":"seed_fe67e4c0bbb686c5e8347ec09e90513f","title":"Reward Engineering: Specifying What We Want","order":4}]},{"id":"seed_538da3bc0ccbd37d3fab29f08eb07443","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Intra-Group Advantage Normalization for Policy Training","order":7,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_f11572f4df3cd111b29c1115c8d584f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":8,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_8ab2ad29bcfe76d03a61e3fdfc68781b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_da79aaad30c9ea311f912f1f9987d531","title":"Problem Formulation","order":0},{"id":"seed_7badc8822a2b15e80fbd40c01c367037","title":"Explicit vs Implicit Feedback","order":1},{"id":"seed_9f37ce8b28608b223711c6938c7552dd","title":"Cold Start Problem","order":2},{"id":"seed_05fe158c39262a24da982d68b162c785","title":"Evaluation Metrics","order":3},{"id":"seed_bfa867934341a6111dcc07ea78e4d2bf","title":"A/B Testing","order":4}]},{"id":"seed_7b67e7eda540fa724d88ee493898b69c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Collaborative Filtering","order":2,"contentType":"LEARNING","contentSlug":"collaborative-filtering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8e114f4d826ccf0ab985de00191f339d","title":"User-based Collaborative Filtering","order":0},{"id":"seed_1a287e127ed7b797307b25696683b668","title":"Item-based Collaborative Filtering","order":1},{"id":"seed_fec56910667f6081593a37b974de8fd4","title":"Similarity Measures","order":2},{"id":"seed_4212728e713835369369a99532daa71e","title":"Neighborhood Methods","order":3},{"id":"seed_dc4ebe51624cefebfa81bdbc180cb66b","title":"Scalability","order":4}]},{"id":"seed_8b451bfeded3345408e779b5bc2b7fb1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Matrix Factorization for Recommendations","order":3,"contentType":"LEARNING","contentSlug":"matrix-factorization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5f57691eb9a54464572ab37699240050","title":"Latent Factor Models","order":0},{"id":"seed_82b8c2b2e7a643aa865b8510c5f457fd","title":"SVD and SVD++","order":1},{"id":"seed_d6452e1d4eec53e60eab0f44b08b0f55","title":"ALS Optimization","order":2},{"id":"seed_cfc7c00993c26515a09b4861d9e34132","title":"Regularization","order":3},{"id":"seed_cc24f9e943ef67af26b2d13aee468672","title":"Implicit Feedback","order":4}]},{"id":"seed_19703d4984d6329646468830d58dec7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Content-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"content-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_942fca4739758c626edbae77a46390af","title":"Item Representations","order":0},{"id":"seed_f5f88433deb2d6eda32b34a0068f85ea","title":"User Profiles","order":1},{"id":"seed_e8cd537403dc19d3a8a734a2e7e35333","title":"TF-IDF and Embeddings","order":2},{"id":"seed_524f46aefda3f7e487ba65082478cc48","title":"Hybrid Approaches","order":3},{"id":"seed_c2b0132208f95f732d98558c2b08d1dd","title":"Knowledge-Based Methods","order":4}]},{"id":"seed_2ab9a2c09cd93ba87112ecc170e53513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Deep Learning for Recommendations","order":5,"contentType":"LEARNING","contentSlug":"deep-learning-for-recommendations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cd55383761648ae19ade5e00fdbdeaaa","title":"Neural Collaborative Filtering","order":0},{"id":"seed_796dda7197932d491d8674bcb2373460","title":"Autoencoders for Recommendations","order":1},{"id":"seed_41ee506f7daa5b266042f506e36820f2","title":"Sequence Models for Recommendations","order":2},{"id":"seed_e4035df4a7dae23acb7cd09c527dbb74","title":"Graph Neural Networks for Recommendations","order":3},{"id":"seed_d49849fa3f7188ee49f9f2628eefbd03","title":"Two-Tower Models","order":4}]},{"id":"seed_2af5ea0db1dad6b70b5104436968ca34","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Production Considerations","order":6,"contentType":"LEARNING","contentSlug":"production-considerations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d0a185b33bbcdd5c756b28e1b4c8b964","title":"Scalability","order":0},{"id":"seed_7bf671fd3047f87c11088ae13eefdba4","title":"Real-time Requirements","order":1},{"id":"seed_dcadbbc9078fdaad2e7b2f925c11f657","title":"Diversity and Fairness","order":2},{"id":"seed_cd36de3d99810f70e3097470e5464818","title":"Exploration-Exploitation","order":3},{"id":"seed_bb173a04f8c5ba1044d6393e46cd6d45","title":"Feedback Loops","order":4}]},{"id":"seed_388dd9ab336383cfe8b60203b6a21a39","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_56dd58a33d5534516199c7580e841f63","title":"Feature Types","order":0},{"id":"seed_842f4a11ba72179bd359958a69c32a08","title":"Domain Knowledge","order":1},{"id":"seed_0e74c9a08deb29e70b8fc92ba3fe99ed","title":"Interaction Features","order":2},{"id":"seed_a46bdb1443418ed68ebb0bc3223d5a9d","title":"Time-Based Features","order":3},{"id":"seed_09805d9c32a742bd2a525f9770161506","title":"Text and Categorical","order":4}]},{"id":"seed_5de18599b9e5181b808780cb9c304da9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Selection Theory","order":2,"contentType":"LEARNING","contentSlug":"feature-selection-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ac96756fe1d66bb7a3dccb4937954afc","title":"Filter Methods","order":0},{"id":"seed_ab79381fc456ad8c97dfdf7bcc88e2d0","title":"Wrapper Methods","order":1},{"id":"seed_cd41c3c79e7e50fd872cb73cfc07fa95","title":"Embedded Methods","order":2},{"id":"seed_392bf51ca8608e808c255ac167e9a796","title":"Stability Selection","order":3},{"id":"seed_ff2defccc0fa65581937fe462a85cd0c","title":"Feature Selection Bias","order":4}]},{"id":"seed_497f3f5dbb61fb7014f109d9f0eb336b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Text Feature Engineering: N-grams","order":3,"contentType":"LEARNING","contentSlug":"text-feature-engineering-ngrams","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3dd8fb7521fc7de44f84462e5a4123a3","title":"Unigrams: The Foundation of Text Feature Engineering","order":0},{"id":"seed_33d61480d27dcd4b92e02bbd8e462e39","title":"Bigrams: Capturing Local Context and Word Pairs","order":1},{"id":"seed_8a5d5e51dba41c7acf29ee19b0efcd45","title":"N-grams: The General Framework for Sequential Text Features","order":2},{"id":"seed_f3c436df144b4698d3527fe8e631cb4b","title":"Character N-grams: Sub-Word Patterns for Robust Text Features","order":3},{"id":"seed_8a62d2081dd9bfaa4e98b712225c6281","title":"N-gram Tradeoffs: A Decision Framework for Text Feature Engineering","order":4}]},{"id":"seed_7f90aa61bc4fb373dafbad502a5f0fd6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":4,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_741941446f08f63cd865ccae73a1a71f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Categorical Binary Encoding","order":5,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_7df14192fe8a798ae765e3b012cede59","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Ordered Polynomial Feature Generator","order":6,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_842f5163e5a439bbac1ef1918b42633a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3397af7317b5277d5ff7122ec986f116","title":"Featuretools: The Foundation of Automated Feature Engineering","order":0},{"id":"seed_c93720908e81eeea4f853b8ba2efab46","title":"Feature Generation: The Complete Taxonomy","order":1},{"id":"seed_7f8e4e3a35e3861f8fc02a717cd30018","title":"Deep Feature Synthesis: The Algorithm Behind Automated FE","order":2},{"id":"seed_e4c80a37f7bee769e6fabbd9ae074e0d","title":"Feature Evaluation: Assessing and Selecting Quality Features","order":3},{"id":"seed_62aabd701415cdeb8d9072e5844a63a9","title":"Computational Considerations: Scaling Automated Feature Engineering","order":4}]},{"id":"seed_81d80261d64ffea6e4533de78d2b9dd0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Stores: The Foundation of Production ML Feature Management","order":2,"contentType":"LEARNING","contentSlug":"feature-stores","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_787216c95406c4c50e1cccdad4f7e772","title":"Feature Store Concept","order":0},{"id":"seed_62ca6df0b94f8119b40371a69912332b","title":"Feast Architecture","order":1},{"id":"seed_326812b4f0808df4c9ebb4bcda549256","title":"Online vs Offline Stores","order":2},{"id":"seed_5353da15321599c127209aa5f6e4ca9d","title":"Feature Reuse","order":3},{"id":"seed_713c7e8ee09618960af8874d2b6ad9ac","title":"Data Consistency","order":4}]},{"id":"seed_f944d98b4893ec36ff4f28076308eeb2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Transformation Pipelines","order":3,"contentType":"LEARNING","contentSlug":"feature-transformation-pipelines","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7b68de2b1bf371c70013fba46412323f","title":"Scikit-learn Pipelines","order":0},{"id":"seed_35aff504fdc34a1c33ff6a3dd3b0df4b","title":"Column Transformers","order":1},{"id":"seed_2d3c19ab28d6a7d8592f4baff3be2871","title":"Custom Transformers","order":2},{"id":"seed_c138507a800c9c01caf55ae68aa95d44","title":"Pipeline Serialization","order":3},{"id":"seed_4aabbdcc5ad82c6b1dadf8ccd7595c30","title":"Production Deployment","order":4}]},{"id":"seed_cc3d8ae2e097a16d9fcc802ad211391b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"High-Cardinality Features","order":4,"contentType":"LEARNING","contentSlug":"high-cardinality-features","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e8a9692fe85c336e537a31efc06aa276","title":"Categorical Encoding","order":0},{"id":"seed_3a5f6e41dc69b628e8a3d3bcb0d0a2cb","title":"Target Encoding","order":1},{"id":"seed_b6f90e488863398b5e581f6af6a8aff0","title":"Embedding Layers","order":2},{"id":"seed_9c0e82c9a6af106f7c4653208d6dca98","title":"Hash Encoding","order":3},{"id":"seed_d3071d36c8bdf27dd53adbc50ee95dc5","title":"Handling Novel Categories","order":4}]},{"id":"seed_a27695ebceaabefd2a3d7ab9d65fb829","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering - Basics","order":5,"contentType":"LEARNING","contentSlug":"text-feature-engineering-basics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a37470f2978780243aa1adee401dd2c5","title":"Bag of Words (BoW)","order":0},{"id":"seed_0a8c02fb8aa1c784bb28cbe17b6dc295","title":"Term Frequency","order":1},{"id":"seed_d411547d24c6279d027de0ee3e4338d4","title":"Vocabulary Construction","order":2},{"id":"seed_64125aa5be53f39a763f024357df3c53","title":"Sparse Representation","order":3},{"id":"seed_1de37c635dfbb87c73249559e726e8fe","title":"BoW Limitations","order":4}]},{"id":"seed_1c77cb034b5085130e3be375725cf1a3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering: TF-IDF","order":6,"contentType":"LEARNING","contentSlug":"text-feature-engineering-tfidf","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9c7581934f8e61cf171b019d4a5f2959","title":"Term Frequency (TF)","order":0},{"id":"seed_3539671e8054f66d418cf5db15c6b707","title":"Inverse Document Frequency (IDF)","order":1},{"id":"seed_25afc0c3d4c696a1952efac83025b717","title":"Why Use Logarithm in IDF","order":2},{"id":"seed_682112f47678f276744f7a07d503cbd7","title":"TF-IDF Weighting","order":3},{"id":"seed_bf9dd12b85180b591ca39e96b13e90f4","title":"Normalization","order":4}]},{"id":"seed_7491c6ce5e01ba00b2b823909fe0d32a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Preprocessing: Transforming Raw Text for Machine Learning","order":7,"contentType":"LEARNING","contentSlug":"text-preprocessing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7cbadb21e2b31a9dd0315fdd172001ad","title":"Tokenization: The Foundation of Text Processing","order":0},{"id":"seed_0df76009024a8c244f8e4edebbf19916","title":"Stop-word Removal: Filtering Noise from Signal","order":1},{"id":"seed_4811497501c2a3593419f69a3fb73bb1","title":"Stemming: Rule-Based Root Extraction","order":2},{"id":"seed_a1c86228d0e56e4e3c9561a164d59970","title":"Lemmatization: Linguistically-Informed Normalization","order":3},{"id":"seed_6dfb9fde8664d1ebcc8d5a84acd886c4","title":"Case Normalization: Unifying Text Representation","order":4},{"id":"seed_08f39130a486e27c3f000e2b77b31116","title":"Punctuation Handling: Managing Text Delimiters","order":5}]},{"id":"seed_7980b68a9f4f240de52a46472d229990","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Word Embeddings","order":8,"contentType":"LEARNING","contentSlug":"word-embeddings","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_313949bc1ac98d9926b8880ce864c9e5","title":"Word2Vec: Skip-gram and CBOW Architectures","order":0},{"id":"seed_b786f03991793dd229cb933d7a88cd32","title":"Average Word2Vec: Document Representation through Vector Averaging","order":1},{"id":"seed_ca3e82787e5cbde8ee221cba1109b267","title":"TF-IDF Weighted Word2Vec: Principled Word Importance Weighting","order":2},{"id":"seed_373581ffe928200559170c7911220d40","title":"GloVe: Global Vectors for Word Representation","order":3},{"id":"seed_af8fa64c1a2408b2174a796a5acbbbe4","title":"FastText: Subword Embeddings for Morphological Richness and OOV Handling","order":4},{"id":"seed_94485eed4514fa4e4a8dd85c63971ece","title":"Pre-trained Embeddings: Practical Strategies for Real-World NLP","order":5}]},{"id":"seed_a9d7121a5834d039ddce6eec079e2e25","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Overview","order":1,"contentType":"LEARNING","contentSlug":"automl-overview","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7542e71a8c159295b15fb3eac6b2a0ab","title":"AutoML Motivation","order":0},{"id":"seed_96c506c7dde6acf11f05452efb550328","title":"What to Automate","order":1},{"id":"seed_21de258b506a5c1e17ce473db86e6c0b","title":"Search Space","order":2},{"id":"seed_145d6e1bc19abcc0c02d60f0b8fe3269","title":"Search Strategies","order":3},{"id":"seed_3c522bdcee806d975c528292d4c8f50e","title":"Evaluation Strategies","order":4}]},{"id":"seed_4822c3e211b027bbc0b9e39d013cd2bd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Preprocessing","order":2,"contentType":"LEARNING","contentSlug":"automated-preprocessing","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b9a2eec6d3ae1b6319657e5afd063c63","title":"Missing Value Handling","order":0},{"id":"seed_b4d95e724ff721c84b4eb4835277047d","title":"Encoding Selection","order":1},{"id":"seed_73cd4522b4aa2d8f73cb6951ffc9dacd","title":"Scaling Selection","order":2},{"id":"seed_0106037f6b6a431abdffebfddeb0d303","title":"Feature Selection","order":3},{"id":"seed_8720af1f18547a30a1fb6ce89f8e22ab","title":"Data Cleaning","order":4}]},{"id":"seed_ae082beb489cc334aefa7f1daded438d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Model Selection","order":3,"contentType":"LEARNING","contentSlug":"automated-model-selection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ffaaa57d1087b8544b058d22bc8da3db","title":"Algorithm Selection","order":0},{"id":"seed_2626021d42d4fcef0932b13fa771dd10","title":"Combined Algorithm Selection and Hyperparameter Optimization (CASH)","order":1},{"id":"seed_ab0c7276e5022476f0e56e2915e5f3a6","title":"Meta-Learning for Selection","order":2},{"id":"seed_4290041bca027ca9130d65afdd0f9af5","title":"Warm Starting","order":3},{"id":"seed_433dc419437b5c80cb73f7d4a5f6346e","title":"Portfolio Methods","order":4}]},{"id":"seed_e1b7d2e67067c90c33a877fa1c26d3e6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Neural Architecture Search","order":4,"contentType":"LEARNING","contentSlug":"neural-architecture-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_01d895ee2a72d00d4df87806cd3162fb","title":"NAS Overview","order":0},{"id":"seed_784450ade551a425c852fa5da276b72a","title":"Search Space Design","order":1},{"id":"seed_d31f73220a65fb1077a8543efafc3ce5","title":"Search Strategies","order":2},{"id":"seed_18ece3a85ea71f09590d4427d1749ba0","title":"Weight Sharing","order":3},{"id":"seed_fd66bdc67d04569dd6b120032330471f","title":"Efficient NAS","order":4}]},{"id":"seed_47b36d588fcad669b42eea822d760f0a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Systems","order":5,"contentType":"LEARNING","contentSlug":"automl-systems","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_999d801f74d27b98ee8a965144b01abd","title":"Auto-sklearn: Automated Machine Learning Through Meta-Learning","order":0},{"id":"seed_40e33223b5d1d10832c94812ce3efd72","title":"AutoGluon: Multi-Layer Stack Ensembling for State-of-the-Art AutoML","order":1},{"id":"seed_944f7b937af1f07a3becf8f516d89f79","title":"H2O AutoML: Enterprise-Grade Automated Machine Learning","order":2},{"id":"seed_bb54014702c4990361531ddb2b7c1bd7","title":"Google Cloud AutoML: Managed AutoML at Scale","order":3},{"id":"seed_bb4cd84056815877f0628e9701ac59a5","title":"AutoML System Comparison: A Decision Framework","order":4}]},{"id":"seed_cb9a45dc1c54e73311b24cd928b3ab69","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Best Practices","order":6,"contentType":"LEARNING","contentSlug":"automl-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1e56c899d3be2441d2775eb02d7c03a2","title":"When to Use AutoML","order":0},{"id":"seed_cf58a8ce44274360741fb76ba3fb5535","title":"Resource Budgets","order":1},{"id":"seed_ffbda19dd65097600ca4aa701c2d781b","title":"Constraint Handling","order":2},{"id":"seed_c730d91ae8cd4cb24d21f9b012b6d48c","title":"Explainability","order":3},{"id":"seed_4abc81b3e277d736edc8dc1c1ef274f6","title":"Production Deployment","order":4}]},{"id":"seed_1458bdc7f12dad04db575fa4a57fe870","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability And Fairness","sectionOrder":58,"title":"Feature Attribution Methods","order":1,"contentType":"LEARNING","contentSlug":"feature-attribution-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7f8b760e1525ac7fa4d8e4dfba277d6c","title":"Permutation Importance","order":0},{"id":"seed_f71c333cf01a9d507cf0aaaf3b5e2671","title":"SHAP Values","order":1},{"id":"seed_e8a6176cfd8277ab46a521a89d831054","title":"Shapley Theory","order":2},{"id":"seed_cceab90cdc080e7cac17ef85c213768d","title":"LIME","order":3},{"id":"seed_243361fd052cdfc38c170d028894c46e","title":"Integrated Gradients","order":4}]},{"id":"seed_0f88251e409f204b5dcec58e017412d4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Interpretability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"interpretability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d5ce117891789cb31c761cb32e70dab6","title":"Why Interpretability","order":0},{"id":"seed_50aa5d581152c5103bc9f5296e2eba99","title":"Intrinsic vs Post-hoc Interpretability","order":1},{"id":"seed_2211b3c8d0505aab86ecba7d5b57790d","title":"Local vs Global Interpretability","order":2},{"id":"seed_6d99987f423504264f7930d36b866a89","title":"Model-Specific vs Model-Agnostic Interpretability","order":3},{"id":"seed_dc43bb2875717a67a46a9d37cbd4eed9","title":"Accuracy-Interpretability Tradeoff","order":4}]},{"id":"seed_09729918b026512bb25f63a10b88afa7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Model-Specific Interpretability","order":2,"contentType":"LEARNING","contentSlug":"model-specific-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5fc6859ed2fd7f82a50090fbd65fbcb6","title":"Linear Model Coefficients","order":0},{"id":"seed_cb22f4ff362a904d45361e7eacaff58f","title":"Tree Visualization","order":1},{"id":"seed_399396af2ffc07e3eb4e424c07bf76f0","title":"Attention Visualization","order":2},{"id":"seed_f70b2a429f82e5ea1355baddb1d48f78","title":"Saliency Maps","order":3},{"id":"seed_f724cc23b5ba2d68bdada80937126b14","title":"Concept Activation Vectors","order":4}]},{"id":"seed_6377599b4a10d0e02c1cfc2dc7aaf743","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Fairness in Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"fairness-in-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_309fc9b026a82738fd0ffd4efa762dea","title":"Fairness Definitions","order":0},{"id":"seed_c9669f3fd3f2489be7876c4c844ad879","title":"Protected Attributes","order":1},{"id":"seed_5dfa7ccb832098884a3318106327aac1","title":"Disparate Impact","order":2},{"id":"seed_47c407bcff666dac87f5a0f33fbb3fa8","title":"Equality of Opportunity","order":3},{"id":"seed_3403c6bb875454aafb8f54dba83207db","title":"Fairness Metrics","order":4}]},{"id":"seed_4951de29801ee8a44a8759a1597460f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Bias Detection and Mitigation","order":4,"contentType":"LEARNING","contentSlug":"bias-detection-mitigation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_97ad248d1c4ad427a7efb88eecf2ba04","title":"Bias Sources","order":0},{"id":"seed_8cc2506a8e01f4c564565f2f85169e9a","title":"Pre-processing Methods","order":1},{"id":"seed_11adbd5188d9672d8890ca7e1170c209","title":"In-processing Methods","order":2},{"id":"seed_1330854c21f83c257eef1c48b773a2f5","title":"Post-processing Methods","order":3},{"id":"seed_ca4190b0e9d08dc9b766f7067e25bad0","title":"Fairness-Accuracy Tradeoffs","order":4}]},{"id":"seed_2a221429a71db5df8da0275ce4697a10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Practical Interpretability","order":5,"contentType":"LEARNING","contentSlug":"practical-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_627159c6c30aac4e0a4ef9b4f107f7fe","title":"Stakeholder Communication","order":0},{"id":"seed_a9b44d22fcc5c785e4175062c1f9106d","title":"Regulatory Requirements","order":1},{"id":"seed_d752805cbccb81d2a11d7a99a2f0be54","title":"Model Cards","order":2},{"id":"seed_3458ab9c5da87704e37311b865e14968","title":"Documentation","order":3},{"id":"seed_47422c41a26465d8552f9efcb4e3bc8e","title":"Auditing","order":4}]},{"id":"seed_a4064079bf9971ca27d16886141b8871","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Foundation Models","order":1,"contentType":"LEARNING","contentSlug":"foundation-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e5ff6409aa980ac554efadd1e94ee4e7","title":"Scale in Machine Learning","order":0},{"id":"seed_a911ffc9a22b3e53bb616705a7199e43","title":"Emergent Capabilities","order":1},{"id":"seed_b76d73e27f6c36569f3a09eadce4df8c","title":"GPT and Large Language Models","order":2},{"id":"seed_2096a2ed0f835a28795305e6de100b0d","title":"Multimodal Models","order":3},{"id":"seed_1922260f859a2dcd43e00f4f2dd4a801","title":"Foundation Model Paradigm","order":4}]},{"id":"seed_50bdd845bc425620c7bd09e44d4f5827","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Large Language Models","order":2,"contentType":"LEARNING","contentSlug":"large-language-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8ccbfbacf3f73e9fc2bb5c6fa02d84ec","title":"Transformer Scaling","order":0},{"id":"seed_a184494d3f9595754b2c0d871563a473","title":"Pre-training Objectives","order":1},{"id":"seed_f9a3ade1f26bafc774a15cb607c99b93","title":"Instruction Tuning","order":2},{"id":"seed_856e49265ee859098fa08fe027599d6e","title":"RLHF (Reinforcement Learning from Human Feedback)","order":3},{"id":"seed_8d48cf51c9b629aacdb2f8d16dc69518","title":"Prompting and In-Context Learning","order":4}]},{"id":"seed_db062e2abf514009df75d477ba2962e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Multimodal Learning: Bridging Vision, Language, and Beyond","order":3,"contentType":"LEARNING","contentSlug":"multimodal-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_21cfd3cc39b3c00fd538566a2b2b970d","title":"Vision-Language Models: Foundations and Architectures","order":0},{"id":"seed_a458d009e0614689105bba3999e3a5a9","title":"CLIP and DALL-E: Revolutionary Vision-Language Models","order":1},{"id":"seed_aaeb719046c90693945174d0b489929e","title":"Audio-Visual Learning: When AI Sees and Hears","order":2},{"id":"seed_2439a1fe23c39c5f86d9f772f8aabbd8","title":"Cross-Modal Retrieval: Searching Across Modalities","order":3},{"id":"seed_70ca6fa7f6f2c91bea9fa02991ff0796","title":"Unified Multimodal Architectures: One Model, Many Modalities","order":4}]},{"id":"seed_e885aaae2eb8f75705604e9d2293f2ec","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Federated Learning","order":4,"contentType":"LEARNING","contentSlug":"federated-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cb2594c497a2ffbe2aa36bb7c6cc095b","title":"Distributed Training: Foundations of Federated Learning","order":0},{"id":"seed_884e5881dae8edbe5a44143acdfe27bf","title":"Privacy Preservation: Rigorous Privacy Guarantees in Federated Learning","order":1},{"id":"seed_4fd909395316a45c9f711ada8942fe27","title":"Communication Efficiency: Reducing Bandwidth in Federated Learning","order":2},{"id":"seed_5b22ed855569eac5c10fb228d06b4879","title":"Heterogeneous Data: Tackling Non-IID Distributions in Federated Learning","order":3},{"id":"seed_4a4b787bcc3975702d21898a316f2e91","title":"Federated Optimization: Algorithms for Decentralized Training","order":4}]},{"id":"seed_f6da44328600f666e7d9a74d1c32b432","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Continual Learning: Learning Without Forgetting","order":5,"contentType":"LEARNING","contentSlug":"continual-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_92a78316ca1bb16c2f40fab2d2518383","title":"Catastrophic Forgetting: The Core Challenge","order":0},{"id":"seed_69b6f03d2835519df087e54057261a58","title":"Regularization Approaches to Continual Learning","order":1},{"id":"seed_42fd7700cdd956638d6ab3cbfdf81ac8","title":"Replay Methods for Continual Learning","order":2},{"id":"seed_2debd5b4dce66b05eb76895a6d0c99ea","title":"Dynamic Architectures for Continual Learning","order":3},{"id":"seed_73f622be84d768dde27c55e1448fef3c","title":"Evaluation Protocols for Continual Learning","order":4}]},{"id":"seed_3efeb1d46a6097c7b10ddb9525c4b581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Emerging Directions in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"emerging-directions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c26a73ba96e34b13cd1f6f890379cea1","title":"Neurosymbolic AI","order":0},{"id":"seed_883ebe97d9e2af44700fe4162805078a","title":"Causal Machine Learning","order":1},{"id":"seed_c4e6f3699d8f21e860a45eb1eccab744","title":"World Models","order":2},{"id":"seed_c3f133751013f2c0c6c18cd5b8d11d14","title":"AI Safety","order":3},{"id":"seed_9bdbf916201809317c606d22144fee34","title":"Efficient ML","order":4}]},{"id":"seed_723cca038a2c40f84476ae0e31ab18e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9c02b1cac102d1618b5d2ec9d3acba59","title":"Requirements Gathering","order":0},{"id":"seed_4dc28980257492a88544dbbe86e73b19","title":"Data Pipeline Design","order":1},{"id":"seed_62a45e62e8b80ee0447ecd615fa712c0","title":"Model Architecture","order":2},{"id":"seed_b7258f0e68a594a5a66f52270fa9899a","title":"Serving Infrastructure","order":3},{"id":"seed_0732cd35ca60ac2d3904f7f048471665","title":"Monitoring and Maintenance","order":4}]},{"id":"seed_dbfa0d7a5ff41a228873f58339420ef9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Debugging ML Models","order":2,"contentType":"LEARNING","contentSlug":"debugging-ml-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d9d2a99058adc397a751811416595e5f","title":"Training Debugging","order":0},{"id":"seed_4271b4ae3eaaa79f36aa687080b09aba","title":"Data Debugging","order":1},{"id":"seed_86cf1003e649a0ded1b3bf5b996cf595","title":"Model Debugging","order":2},{"id":"seed_f226e71a2171e33f686c6786b4d49392","title":"Performance Debugging","order":3},{"id":"seed_fd41a6a1a89a4d5ddcc8aa0a4dedc876","title":"Systematic Debugging","order":4}]},{"id":"seed_29daeedf46dc1d31c9070e587276c581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Project Management","order":3,"contentType":"LEARNING","contentSlug":"ml-project-management","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_877ea1e28d3391ee7bbb06ebedda76b6","title":"Problem Scoping","order":0},{"id":"seed_ef3cfcdc33e9e1e026cfd75d00380c8f","title":"Data Collection Planning","order":1},{"id":"seed_25075dce25ecddadca46f876a40d36f0","title":"Experiment Management","order":2},{"id":"seed_9e6bfb02646e5dca43d4dcc3111e7182","title":"Iteration Strategies","order":3},{"id":"seed_4ff1ad1d9656b082b07548425a47280a","title":"Launch Criteria","order":4}]},{"id":"seed_800ab48cf1731556a258ea5ec18b78f6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Reading Research Papers","order":4,"contentType":"LEARNING","contentSlug":"reading-research-papers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e4a60c573f82838aac6f77ba4858a2c2","title":"Paper Structure","order":0},{"id":"seed_1ff1780965108efee6996e30b65aeebf","title":"Critical Reading","order":1},{"id":"seed_bf84ef490bfeb38c9d31de6585c6bb9f","title":"Reproducing Results","order":2},{"id":"seed_db748e29a14347a7ef6d817001e9cdb7","title":"Staying Current","order":3},{"id":"seed_0c63aa37941655ef50378149ce6ee0e9","title":"Research Taste","order":4}]},{"id":"seed_f292b4c2034d13213b3e7c675b18f765","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Interviews: Complete Preparation Guide","order":5,"contentType":"LEARNING","contentSlug":"ml-interviews","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4cf930be608fbe9c6aca6e6eb103f2f3","title":"ML Interview Types: A Complete Taxonomy","order":0},{"id":"seed_1b7e561e9cbcadbd9cb99da6b2645565","title":"Technical Preparation: A Strategic Approach","order":1},{"id":"seed_49c4bddac8288cb3ac3494b82e63b4ea","title":"Coding Interviews: Mastery Guide for ML Roles","order":2},{"id":"seed_7b925848fb34013f6b55161466bc4847","title":"ML Design Interviews: System Architecture Mastery","order":3},{"id":"seed_e476ec333b423888c3ff6981da80f6ed","title":"Common ML Interview Questions: The Essential Reference","order":4}]},{"id":"seed_632dc2189cf690119e3942c3dfa69d8b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Career Development in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"career-development","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_26c147dafb9cdd140c576d97b69cf6c3","title":"ML Career Paths","order":0},{"id":"seed_1d664a155647c95db0e8933179063636","title":"Skill Development","order":1},{"id":"seed_eed0cee99e2d5bf6562ca0f1c541be58","title":"Building Portfolio","order":2},{"id":"seed_61b1f72e33d35a7e5c26127c6fcb33dc","title":"Community Engagement","order":3},{"id":"seed_1be2e204910fc41848aa81e200d02788","title":"Continuous Learning","order":4}]},{"id":"seed_39cac80dede8b891307a5865480e576b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Dataset Batch Generator for Training Pipelines","order":7,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_76bec3396a0c10a07b961c0659f31f5b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Performance Metrics Calculator","order":8,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_d8bc35e4e84cf3a27ee560839206c155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Request Batching","order":9,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_07789f50da99325787e488c27c2f335e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Synchronized Data Permutation","order":1,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_0818b627401c3528be0e63d2b248925b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"One bad dataset row consumes several quality allowances","order":2,"contentType":"REPO","contentSlug":"dataset-gate-failure-budget","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_76aa74612887d6b05ec63a158ebb9b68","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Count bad dataset rows once","order":3,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-failure-budget","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_82746decf240c4d206937e214136ffe6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Published datasets differ from the snapshot that passed review","order":4,"contentType":"REPO","contentSlug":"dataset-gate-unchecked-publication","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6a6c35120fbb3874411dee3e7217348d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Publish the rows captured by the quality run","order":5,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-unchecked-publication","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]}]},"currentModule":"$46:props:state:queries:1:state:data:json:path:modules:316","prevModule":"$46:props:state:queries:1:state:data:json:path:modules:315","nextModule":"$46:props:state:queries:1:state:data:json:path:modules:317","allModules":"$46:props:state:queries:1:state:data:json:path:modules"},"meta":{"values":{"path.createdAt":["Date"],"path.updatedAt":["Date"],"path.modules.0.createdAt":["Date"],"path.modules.0.updatedAt":["Date"],"path.modules.1.createdAt":["Date"],"path.modules.1.updatedAt":["Date"],"path.modules.2.createdAt":["Date"],"path.modules.2.updatedAt":["Date"],"path.modules.3.createdAt":["Date"],"path.modules.3.updatedAt":["Date"],"path.modules.4.createdAt":["Date"],"path.modules.4.updatedAt":["Date"],"path.modules.5.createdAt":["Date"],"path.modules.5.updatedAt":["Date"],"path.modules.6.createdAt":["Date"],"path.modules.6.updatedAt":["Date"],"path.modules.7.createdAt":["Date"],"path.modules.7.updatedAt":["Date"],"path.modules.8.createdAt":["Date"],"path.modules.8.updatedAt":["Date"],"path.modules.9.createdAt":["Date"],"path.modules.9.updatedAt":["Date"],"path.modules.10.createdAt":["Date"],"path.modules.10.updatedAt":["Date"],"path.modules.11.createdAt":["Date"],"path.modules.11.updatedAt":["Date"],"path.modules.12.createdAt":["Date"],"path.modules.12.updatedAt":["Date"],"path.modules.13.createdAt":["Date"],"path.modules.13.updatedAt":["Date"],"path.modules.14.createdAt":["Date"],"path.modules.14.updatedAt":["Date"],"path.modules.15.createdAt":["Date"],"path.modules.15.updatedAt":["Date"],"path.modules.16.createdAt":["Date"],"path.modules.16.updatedAt":["Date"],"path.modules.17.createdAt":["Date"],"path.modules.17.updatedAt":["Date"],"path.modules.18.createdAt":["Date"],"path.modules.18.updatedAt":["Date"],"path.modules.19.createdAt":["Date"],"path.modules.19.updatedAt":["Date"],"path.modules.20.createdAt":["Date"],"path.modules.20.updatedAt":["Date"],"path.modules.21.createdAt":["Date"],"path.modules.21.updatedAt":["Date"],"path.modules.22.createdAt":["Date"],"path.modules.22.updatedAt":["Date"],"path.modules.23.createdAt":["Date"],"path.modules.23.updatedAt":["Date"],"path.modules.24.createdAt":["Date"],"path.modules.24.updatedAt":["Date"],"path.modules.25.createdAt":["Date"],"path.modules.25.updatedAt":["Date"],"path.modules.26.createdAt":["Date"],"path.modules.26.updatedAt":["Date"],"path.modules.27.createdAt":["Date"],"path.modules.27.updatedAt":["Date"],"path.modules.28.createdAt":["Date"],"path.modules.28.updatedAt":["Date"],"path.modules.29.createdAt":["Date"],"path.modules.29.updatedAt":["Date"],"path.modules.30.createdAt":["Date"],"path.modules.30.updatedAt":["Date"],"path.modules.31.createdAt":["Date"],"path.modules.31.updatedAt":["Date"],"path.modules.32.createdAt":["Date"],"path.modules.32.updatedAt":["Date"],"path.modules.33.createdAt":["Date"],"path.modules.33.updatedAt":["Date"],"path.modules.34.createdAt":["Date"],"path.modules.34.updatedAt":["Date"],"path.modules.35.createdAt":["Date"],"path.modules.35.updatedAt":["Date"],"path.modules.36.createdAt":["Date"],"path.modules.36.updatedAt":["Date"],"path.modules.37.createdAt":["Date"],"path.modules.37.updatedAt":["Date"],"path.modules.38.createdAt":["Date"],"path.modules.38.updatedAt":["Date"],"path.modules.39.createdAt":["Date"],"path.modules.39.updatedAt":["Date"],"path.modules.40.createdAt":["Date"],"path.modules.40.updatedAt":["Date"],"path.modules.41.createdAt":["Date"],"path.modules.41.updatedAt":["Date"],"path.modules.42.createdAt":["Date"],"path.modules.42.updatedAt":["Date"],"path.modules.43.createdAt":["Date"],"path.modules.43.updatedAt":["Date"],"path.modules.44.createdAt":["Date"],"path.modules.44.updatedAt":["Date"],"path.modules.45.createdAt":["Date"],"path.modules.45.updatedAt":["Date"],"path.modules.46.createdAt":["Date"],"path.modules.46.updatedAt":["Date"],"path.modules.47.createdAt":["Date"],"path.modules.47.updatedAt":["Date"],"path.modules.48.createdAt":["Date"],"path.modules.48.updatedAt":["Date"],"path.modules.49.createdAt":["Date"],"path.modules.49.updatedAt":["Date"],"path.modules.50.createdAt":["Date"],"path.modules.50.updatedAt":["Date"],"path.modules.51.createdAt":["Date"],"path.modules.51.updatedAt":["Date"],"path.modules.52.createdAt":["Date"],"path.modules.52.updatedAt":["Date"],"path.modules.53.createdAt":["Date"],"path.modules.53.updatedAt":["Date"],"path.modules.54.createdAt":["Date"],"path.modules.54.updatedAt":["Date"],"path.modules.55.createdAt":["Date"],"path.modules.55.updatedAt":["Date"],"path.modules.56.createdAt":["Date"],"path.modules.56.updatedAt":["Date"],"path.modules.57.createdAt":["Date"],"path.modules.57.updatedAt":["Date"],"path.modules.58.createdAt":["Date"],"path.modules.58.updatedAt":["Date"],"path.modules.59.createdAt":["Date"],"path.modules.59.updatedAt":["Date"],"path.modules.60.createdAt":["Date"],"path.modules.60.updatedAt":["Date"],"path.modules.61.createdAt":["Date"],"path.modules.61.updatedAt":["Date"],"path.modules.62.createdAt":["Date"],"path.modules.62.updatedAt":["Date"],"path.modules.63.createdAt":["Date"],"path.modules.63.updatedAt":["Date"],"path.modules.64.createdAt":["Date"],"path.modules.64.updatedAt":["Date"],"path.modules.65.createdAt":["Date"],"path.modules.65.updatedAt":["Date"],"path.modules.66.createdAt":["Date"],"path.modules.66.updatedAt":["Date"],"path.modules.67.createdAt":["Date"],"path.modules.67.updatedAt":["Date"],"path.modules.68.createdAt":["Date"],"path.modules.68.updatedAt":["Date"],"path.modules.69.createdAt":["Date"],"path.modules.69.updatedAt":["Date"],"path.modules.70.createdAt":["Date"],"path.modules.70.updatedAt":["Date"],"path.modules.71.createdAt":["Date"],"path.modules.71.updatedAt":["Date"],"path.modules.72.createdAt":["Date"],"path.modules.72.updatedAt":["Date"],"path.modules.73.createdAt":["Date"],"path.modules.73.updatedAt":["Date"],"path.modules.74.createdAt":["Date"],"path.modules.74.updatedAt":["Date"],"path.modules.75.createdAt":["Date"],"path.modules.75.updatedAt":["Date"],"path.modules.76.createdAt":["Date"],"path.modules.76.updatedAt":["Date"],"path.modules.77.createdAt":["Date"],"path.modules.77.updatedAt":["Date"],"path.modules.78.createdAt":["Date"],"path.modules.78.updatedAt":["Date"],"path.modules.79.createdAt":["Date"],"path.modules.79.updatedAt":["Date"],"path.modules.80.createdAt":["Date"],"path.modules.80.updatedAt":["Date"],"path.modules.81.createdAt":["Date"],"path.modules.81.updatedAt":["Date"],"path.modules.82.createdAt":["Date"],"path.modules.82.updatedAt":["Date"],"path.modules.83.createdAt":["Date"],"path.modules.83.updatedAt":["Date"],"path.modules.84.createdAt":["Date"],"path.modules.84.updatedAt":["Date"],"path.modules.85.createdAt":["Date"],"path.modules.85.updatedAt":["Date"],"path.modules.86.createdAt":["Date"],"path.modules.86.updatedAt":["Date"],"path.modules.87.createdAt":["Date"],"path.modules.87.updatedAt":["Date"],"path.modules.88.createdAt":["Date"],"path.modules.88.updatedAt":["Date"],"path.modules.89.createdAt":["Date"],"path.modules.89.updatedAt":["Date"],"path.modules.90.createdAt":["Date"],"path.modules.90.updatedAt":["Date"],"path.modules.91.createdAt":["Date"],"path.modules.91.updatedAt":["Date"],"path.modules.92.createdAt":["Date"],"path.modules.92.updatedAt":["Date"],"path.modules.93.createdAt":["Date"],"path.modules.93.updatedAt":["Date"],"path.modules.94.createdAt":["Date"],"path.modules.94.updatedAt":["Date"],"path.modules.95.createdAt":["Date"],"path.modules.95.updatedAt":["Date"],"path.modules.96.createdAt":["Date"],"path.modules.96.updatedAt":["Date"],"path.modules.97.createdAt":["Date"],"path.modules.97.updatedAt":["Date"],"path.modules.98.createdAt":["Date"],"path.modules.98.updatedAt":["Date"],"path.modules.99.createdAt":["Date"],"path.modules.99.updatedAt":["Date"],"path.modules.100.createdAt":["Date"],"path.modules.100.updatedAt":["Date"],"path.modules.101.createdAt":["Date"],"path.modules.101.updatedAt":["Date"],"path.modules.102.createdAt":["Date"],"path.modules.102.updatedAt":["Date"],"path.modules.103.createdAt":["Date"],"path.modules.103.updatedAt":["Date"],"path.modules.104.createdAt":["Date"],"path.modules.104.updatedAt":["Date"],"path.modules.105.createdAt":["Date"],"path.modules.105.updatedAt":["Date"],"path.modules.106.createdAt":["Date"],"path.modules.106.updatedAt":["Date"],"path.modules.107.createdAt":["Date"],"path.modules.107.updatedAt":["Date"],"path.modules.108.createdAt":["Date"],"path.modules.108.updatedAt":["Date"],"path.modules.109.createdAt":["Date"],"path.modules.109.updatedAt":["Date"],"path.modules.110.createdAt":["Date"],"path.modules.110.updatedAt":["Date"],"path.modules.111.createdAt":["Date"],"path.modules.111.updatedAt":["Date"],"path.modules.112.createdAt":["Date"],"path.modules.112.updatedAt":["Date"],"path.modules.113.createdAt":["Date"],"path.modules.113.updatedAt":["Date"],"path.modules.114.createdAt":["Date"],"path.modules.114.updatedAt":["Date"],"path.modules.115.createdAt":["Date"],"path.modules.115.updatedAt":["Date"],"path.modules.116.createdAt":["Date"],"path.modules.116.updatedAt":["Date"],"path.modules.117.createdAt":["Date"],"path.modules.117.updatedAt":["Date"],"path.modules.118.createdAt":["Date"],"path.modules.118.updatedAt":["Date"],"path.modules.119.createdAt":["Date"],"path.modules.119.updatedAt":["Date"],"path.modules.120.createdAt":["Date"],"path.modules.120.updatedAt":["Date"],"path.modules.121.createdAt":["Date"],"path.modules.121.updatedAt":["Date"],"path.modules.122.createdAt":["Date"],"path.modules.122.updatedAt":["Date"],"path.modules.123.createdAt":["Date"],"path.modules.123.updatedAt":["Date"],"path.modules.124.createdAt":["Date"],"path.modules.124.updatedAt":["Date"],"path.modules.125.createdAt":["Date"],"path.modules.125.updatedAt":["Date"],"path.modules.126.createdAt":["Date"],"path.modules.126.updatedAt":["Date"],"path.modules.127.createdAt":["Date"],"path.modules.127.updatedAt":["Date"],"path.modules.128.createdAt":["Date"],"path.modules.128.updatedAt":["Date"],"path.modules.129.createdAt":["Date"],"path.modules.129.updatedAt":["Date"],"path.modules.130.createdAt":["Date"],"path.modules.130.updatedAt":["Date"],"path.modules.131.createdAt":["Date"],"path.modules.131.updatedAt":["Date"],"path.modules.132.createdAt":["Date"],"path.modules.132.updatedAt":["Date"],"path.modules.133.createdAt":["Date"],"path.modules.133.updatedAt":["Date"],"path.modules.134.createdAt":["Date"],"path.modules.134.updatedAt":["Date"],"path.modules.135.createdAt":["Date"],"path.modules.135.updatedAt":["Date"],"path.modules.136.createdAt":["Date"],"path.modules.136.updatedAt":["Date"],"path.modules.137.createdAt":["Date"],"path.modules.137.updatedAt":["Date"],"path.modules.138.createdAt":["Date"],"path.modules.138.updatedAt":["Date"],"path.modules.139.createdAt":["Date"],"path.modules.139.updatedAt":["Date"],"path.modules.140.createdAt":["Date"],"path.modules.140.updatedAt":["Date"],"path.modules.141.createdAt":["Date"],"path.modules.141.updatedAt":["Date"],"path.modules.142.createdAt":["Date"],"path.modules.142.updatedAt":["Date"],"path.modules.143.createdAt":["Date"],"path.modules.143.updatedAt":["Date"],"path.modules.144.createdAt":["Date"],"path.modules.144.updatedAt":["Date"],"path.modules.145.createdAt":["Date"],"path.modules.145.updatedAt":["Date"],"path.modules.146.createdAt":["Date"],"path.modules.146.updatedAt":["Date"],"path.modules.147.createdAt":["Date"],"path.modules.147.updatedAt":["Date"],"path.modules.148.createdAt":["Date"],"path.modules.148.updatedAt":["Date"],"path.modules.149.createdAt":["Date"],"path.modules.149.updatedAt":["Date"],"path.modules.150.createdAt":["Date"],"path.modules.150.updatedAt":["Date"],"path.modules.151.createdAt":["Date"],"path.modules.151.updatedAt":["Date"],"path.modules.152.createdAt":["Date"],"path.modules.152.updatedAt":["Date"],"path.modules.153.createdAt":["Date"],"path.modules.153.updatedAt":["Date"],"path.modules.154.createdAt":["Date"],"path.modules.154.updatedAt":["Date"],"path.modules.155.createdAt":["Date"],"path.modules.155.updatedAt":["Date"],"path.modules.156.createdAt":["Date"],"path.modules.156.updatedAt":["Date"],"path.modules.157.createdAt":["Date"],"path.modules.157.updatedAt":["Date"],"path.modules.158.createdAt":["Date"],"path.modules.158.updatedAt":["Date"],"path.modules.159.createdAt":["Date"],"path.modules.159.updatedAt":["Date"],"path.modules.160.createdAt":["Date"],"path.modules.160.updatedAt":["Date"],"path.modules.161.createdAt":["Date"],"path.modules.161.updatedAt":["Date"],"path.modules.162.createdAt":["Date"],"path.modules.162.updatedAt":["Date"],"path.modules.163.createdAt":["Date"],"path.modules.163.updatedAt":["Date"],"path.modules.164.createdAt":["Date"],"path.modules.164.updatedAt":["Date"],"path.modules.165.createdAt":["Date"],"path.modules.165.updatedAt":["Date"],"path.modules.166.createdAt":["Date"],"path.modules.166.updatedAt":["Date"],"path.modules.167.createdAt":["Date"],"path.modules.167.updatedAt":["Date"],"path.modules.168.createdAt":["Date"],"path.modules.168.updatedAt":["Date"],"path.modules.169.createdAt":["Date"],"path.modules.169.updatedAt":["Date"],"path.modules.170.createdAt":["Date"],"path.modules.170.updatedAt":["Date"],"path.modules.171.createdAt":["Date"],"path.modules.171.updatedAt":["Date"],"path.modules.172.createdAt":["Date"],"path.modules.172.updatedAt":["Date"],"path.modules.173.createdAt":["Date"],"path.modules.173.updatedAt":["Date"],"path.modules.174.createdAt":["Date"],"path.modules.174.updatedAt":["Date"],"path.modules.175.createdAt":["Date"],"path.modules.175.updatedAt":["Date"],"path.modules.176.createdAt":["Date"],"path.modules.176.updatedAt":["Date"],"path.modules.177.createdAt":["Date"],"path.modules.177.updatedAt":["Date"],"path.modules.178.createdAt":["Date"],"path.modules.178.updatedAt":["Date"],"path.modules.179.createdAt":["Date"],"path.modules.179.updatedAt":["Date"],"path.modules.180.createdAt":["Date"],"path.modules.180.updatedAt":["Date"],"path.modules.181.createdAt":["Date"],"path.modules.181.updatedAt":["Date"],"path.modules.182.createdAt":["Date"],"path.modules.182.updatedAt":["Date"],"path.modules.183.createdAt":["Date"],"path.modules.183.updatedAt":["Date"],"path.modules.184.createdAt":["Date"],"path.modules.184.updatedAt":["Date"],"path.modules.185.createdAt":["Date"],"path.modules.185.updatedAt":["Date"],"path.modules.186.createdAt":["Date"],"path.modules.186.updatedAt":["Date"],"path.modules.187.createdAt":["Date"],"path.modules.187.updatedAt":["Date"],"path.modules.188.createdAt":["Date"],"path.modules.188.updatedAt":["Date"],"path.modules.189.createdAt":["Date"],"path.modules.189.updatedAt":["Date"],"path.modules.190.createdAt":["Date"],"path.modules.190.updatedAt":["Date"],"path.modules.191.createdAt":["Date"],"path.modules.191.updatedAt":["Date"],"path.modules.192.createdAt":["Date"],"path.modules.192.updatedAt":["Date"],"path.modules.193.createdAt":["Date"],"path.modules.193.updatedAt":["Date"],"path.modules.194.createdAt":["Date"],"path.modules.194.updatedAt":["Date"],"path.modules.195.createdAt":["Date"],"path.modules.195.updatedAt":["Date"],"path.modules.196.createdAt":["Date"],"path.modules.196.updatedAt":["Date"],"path.modules.197.createdAt":["Date"],"path.modules.197.updatedAt":["Date"],"path.modules.198.createdAt":["Date"],"path.modules.198.updatedAt":["Date"],"path.modules.199.createdAt":["Date"],"path.modules.199.updatedAt":["Date"],"path.modules.200.createdAt":["Date"],"path.modules.200.updatedAt":["Date"],"path.modules.201.createdAt":["Date"],"path.modules.201.updatedAt":["Date"],"path.modules.202.createdAt":["Date"],"path.modules.202.updatedAt":["Date"],"path.modules.203.createdAt":["Date"],"path.modules.203.updatedAt":["Date"],"path.modules.204.createdAt":["Date"],"path.modules.204.updatedAt":["Date"],"path.modules.205.createdAt":["Date"],"path.modules.205.updatedAt":["Date"],"path.modules.206.createdAt":["Date"],"path.modules.206.updatedAt":["Date"],"path.modules.207.createdAt":["Date"],"path.modules.207.updatedAt":["Date"],"path.modules.208.createdAt":["Date"],"path.modules.208.updatedAt":["Date"],"path.modules.209.createdAt":["Date"],"path.modules.209.updatedAt":["Date"],"path.modules.210.createdAt":["Date"],"path.modules.210.updatedAt":["Date"],"path.modules.211.createdAt":["Date"],"path.modules.211.updatedAt":["Date"],"path.modules.212.createdAt":["Date"],"path.modules.212.updatedAt":["Date"],"path.modules.213.createdAt":["Date"],"path.modules.213.updatedAt":["Date"],"path.modules.214.createdAt":["Date"],"path.modules.214.updatedAt":["Date"],"path.modules.215.createdAt":["Date"],"path.modules.215.updatedAt":["Date"],"path.modules.216.createdAt":["Date"],"path.modules.216.updatedAt":["Date"],"path.modules.217.createdAt":["Date"],"path.modules.217.updatedAt":["Date"],"path.modules.218.createdAt":["Date"],"path.modules.218.updatedAt":["Date"],"path.modules.219.createdAt":["Date"],"path.modules.219.updatedAt":["Date"],"path.modules.220.createdAt":["Date"],"path.modules.220.updatedAt":["Date"],"path.modules.221.createdAt":["Date"],"path.modules.221.updatedAt":["Date"],"path.modules.222.createdAt":["Date"],"path.modules.222.updatedAt":["Date"],"path.modules.223.createdAt":["Date"],"path.modules.223.updatedAt":["Date"],"path.modules.224.createdAt":["Date"],"path.modules.224.updatedAt":["Date"],"path.modules.225.createdAt":["Date"],"path.modules.225.updatedAt":["Date"],"path.modules.226.createdAt":["Date"],"path.modules.226.updatedAt":["Date"],"path.modules.227.createdAt":["Date"],"path.modules.227.updatedAt":["Date"],"path.modules.228.createdAt":["Date"],"path.modules.228.updatedAt":["Date"],"path.modules.229.createdAt":["Date"],"path.modules.229.updatedAt":["Date"],"path.modules.230.createdAt":["Date"],"path.modules.230.updatedAt":["Date"],"path.modules.231.createdAt":["Date"],"path.modules.231.updatedAt":["Date"],"path.modules.232.createdAt":["Date"],"path.modules.232.updatedAt":["Date"],"path.modules.233.createdAt":["Date"],"path.modules.233.updatedAt":["Date"],"path.modules.234.createdAt":["Date"],"path.modules.234.updatedAt":["Date"],"path.modules.235.createdAt":["Date"],"path.modules.235.updatedAt":["Date"],"path.modules.236.createdAt":["Date"],"path.modules.236.updatedAt":["Date"],"path.modules.237.createdAt":["Date"],"path.modules.237.updatedAt":["Date"],"path.modules.238.createdAt":["Date"],"path.modules.238.updatedAt":["Date"],"path.modules.239.createdAt":["Date"],"path.modules.239.updatedAt":["Date"],"path.modules.240.createdAt":["Date"],"path.modules.240.updatedAt":["Date"],"path.modules.241.createdAt":["Date"],"path.modules.241.updatedAt":["Date"],"path.modules.242.createdAt":["Date"],"path.modules.242.updatedAt":["Date"],"path.modules.243.createdAt":["Date"],"path.modules.243.updatedAt":["Date"],"path.modules.244.createdAt":["Date"],"path.modules.244.updatedAt":["Date"],"path.modules.245.createdAt":["Date"],"path.modules.245.updatedAt":["Date"],"path.modules.246.createdAt":["Date"],"path.modules.246.updatedAt":["Date"],"path.modules.247.createdAt":["Date"],"path.modules.247.updatedAt":["Date"],"path.modules.248.createdAt":["Date"],"path.modules.248.updatedAt":["Date"],"path.modules.249.createdAt":["Date"],"path.modules.249.updatedAt":["Date"],"path.modules.250.createdAt":["Date"],"path.modules.250.updatedAt":["Date"],"path.modules.251.createdAt":["Date"],"path.modules.251.updatedAt":["Date"],"path.modules.252.createdAt":["Date"],"path.modules.252.updatedAt":["Date"],"path.modules.253.createdAt":["Date"],"path.modules.253.updatedAt":["Date"],"path.modules.254.createdAt":["Date"],"path.modules.254.updatedAt":["Date"],"path.modules.255.createdAt":["Date"],"path.modules.255.updatedAt":["Date"],"path.modules.256.createdAt":["Date"],"path.modules.256.updatedAt":["Date"],"path.modules.257.createdAt":["Date"],"path.modules.257.updatedAt":["Date"],"path.modules.258.createdAt":["Date"],"path.modules.258.updatedAt":["Date"],"path.modules.259.createdAt":["Date"],"path.modules.259.updatedAt":["Date"],"path.modules.260.createdAt":["Date"],"path.modules.260.updatedAt":["Date"],"path.modules.261.createdAt":["Date"],"path.modules.261.updatedAt":["Date"],"path.modules.262.createdAt":["Date"],"path.modules.262.updatedAt":["Date"],"path.modules.263.createdAt":["Date"],"path.modules.263.updatedAt":["Date"],"path.modules.264.createdAt":["Date"],"path.modules.264.updatedAt":["Date"],"path.modules.265.createdAt":["Date"],"path.modules.265.updatedAt":["Date"],"path.modules.266.createdAt":["Date"],"path.modules.266.updatedAt":["Date"],"path.modules.267.createdAt":["Date"],"path.modules.267.updatedAt":["Date"],"path.modules.268.createdAt":["Date"],"path.modules.268.updatedAt":["Date"],"path.modules.269.createdAt":["Date"],"path.modules.269.updatedAt":["Date"],"path.modules.270.createdAt":["Date"],"path.modules.270.updatedAt":["Date"],"path.modules.271.createdAt":["Date"],"path.modules.271.updatedAt":["Date"],"path.modules.272.createdAt":["Date"],"path.modules.272.updatedAt":["Date"],"path.modules.273.createdAt":["Date"],"path.modules.273.updatedAt":["Date"],"path.modules.274.createdAt":["Date"],"path.modules.274.updatedAt":["Date"],"path.modules.275.createdAt":["Date"],"path.modules.275.updatedAt":["Date"],"path.modules.276.createdAt":["Date"],"path.modules.276.updatedAt":["Date"],"path.modules.277.createdAt":["Date"],"path.modules.277.updatedAt":["Date"],"path.modules.278.createdAt":["Date"],"path.modules.278.updatedAt":["Date"],"path.modules.279.createdAt":["Date"],"path.modules.279.updatedAt":["Date"],"path.modules.280.createdAt":["Date"],"path.modules.280.updatedAt":["Date"],"path.modules.281.createdAt":["Date"],"path.modules.281.updatedAt":["Date"],"path.modules.282.createdAt":["Date"],"path.modules.282.updatedAt":["Date"],"path.modules.283.createdAt":["Date"],"path.modules.283.updatedAt":["Date"],"path.modules.284.createdAt":["Date"],"path.modules.284.updatedAt":["Date"],"path.modules.285.createdAt":["Date"],"path.modules.285.updatedAt":["Date"],"path.modules.286.createdAt":["Date"],"path.modules.286.updatedAt":["Date"],"path.modules.287.createdAt":["Date"],"path.modules.287.updatedAt":["Date"],"path.modules.288.createdAt":["Date"],"path.modules.288.updatedAt":["Date"],"path.modules.289.createdAt":["Date"],"path.modules.289.updatedAt":["Date"],"path.modules.290.createdAt":["Date"],"path.modules.290.updatedAt":["Date"],"path.modules.291.createdAt":["Date"],"path.modules.291.updatedAt":["Date"],"path.modules.292.createdAt":["Date"],"path.modules.292.updatedAt":["Date"],"path.modules.293.createdAt":["Date"],"path.modules.293.updatedAt":["Date"],"path.modules.294.createdAt":["Date"],"path.modules.294.updatedAt":["Date"],"path.modules.295.createdAt":["Date"],"path.modules.295.updatedAt":["Date"],"path.modules.296.createdAt":["Date"],"path.modules.296.updatedAt":["Date"],"path.modules.297.createdAt":["Date"],"path.modules.297.updatedAt":["Date"],"path.modules.298.createdAt":["Date"],"path.modules.298.updatedAt":["Date"],"path.modules.299.createdAt":["Date"],"path.modules.299.updatedAt":["Date"],"path.modules.300.createdAt":["Date"],"path.modules.300.updatedAt":["Date"],"path.modules.301.createdAt":["Date"],"path.modules.301.updatedAt":["Date"],"path.modules.302.createdAt":["Date"],"path.modules.302.updatedAt":["Date"],"path.modules.303.createdAt":["Date"],"path.modules.303.updatedAt":["Date"],"path.modules.304.createdAt":["Date"],"path.modules.304.updatedAt":["Date"],"path.modules.305.createdAt":["Date"],"path.modules.305.updatedAt":["Date"],"path.modules.306.createdAt":["Date"],"path.modules.306.updatedAt":["Date"],"path.modules.307.createdAt":["Date"],"path.modules.307.updatedAt":["Date"],"path.modules.308.createdAt":["Date"],"path.modules.308.updatedAt":["Date"],"path.modules.309.createdAt":["Date"],"path.modules.309.updatedAt":["Date"],"path.modules.310.createdAt":["Date"],"path.modules.310.updatedAt":["Date"],"path.modules.311.createdAt":["Date"],"path.modules.311.updatedAt":["Date"],"path.modules.312.createdAt":["Date"],"path.modules.312.updatedAt":["Date"],"path.modules.313.createdAt":["Date"],"path.modules.313.updatedAt":["Date"],"path.modules.314.createdAt":["Date"],"path.modules.314.updatedAt":["Date"],"path.modules.315.createdAt":["Date"],"path.modules.315.updatedAt":["Date"],"path.modules.316.createdAt":["Date"],"path.modules.316.updatedAt":["Date"],"path.modules.317.createdAt":["Date"],"path.modules.317.updatedAt":["Date"],"path.modules.318.createdAt":["Date"],"path.modules.318.updatedAt":["Date"],"path.modules.319.createdAt":["Date"],"path.modules.319.updatedAt":["Date"],"path.modules.320.createdAt":["Date"],"path.modules.320.updatedAt":["Date"],"path.modules.321.createdAt":["Date"],"path.modules.321.updatedAt":["Date"],"path.modules.322.createdAt":["Date"],"path.modules.322.updatedAt":["Date"],"path.modules.323.createdAt":["Date"],"path.modules.323.updatedAt":["Date"],"path.modules.324.createdAt":["Date"],"path.modules.324.updatedAt":["Date"],"path.modules.325.createdAt":["Date"],"path.modules.325.updatedAt":["Date"],"path.modules.326.createdAt":["Date"],"path.modules.326.updatedAt":["Date"],"path.modules.327.createdAt":["Date"],"path.modules.327.updatedAt":["Date"],"currentModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.316.createdAt","currentModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.316.updatedAt","prevModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.315.createdAt","prevModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.315.updatedAt","nextModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.317.createdAt","nextModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.317.updatedAt","allModules.0.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.0.createdAt","allModules.0.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.0.updatedAt","allModules.1.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.1.createdAt","allModules.1.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.1.updatedAt","allModules.2.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.2.createdAt","allModules.2.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.2.updatedAt","allModules.3.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.3.createdAt","allModules.3.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.3.updatedAt","allModules.4.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.4.createdAt","allModules.4.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.4.updatedAt","allModules.5.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.5.createdAt","allModules.5.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.5.updatedAt","allModules.6.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.6.createdAt","allModules.6.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.6.updatedAt","allModules.7.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.7.createdAt","allModules.7.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.7.updatedAt","allModules.8.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.8.createdAt","allModules.8.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.8.updatedAt","allModules.9.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.9.createdAt","allModules.9.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.9.updatedAt","allModules.10.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.10.createdAt","allModules.10.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.10.updatedAt","allModules.11.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.11.createdAt","allModules.11.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.11.updatedAt","allModules.12.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.12.createdAt","allModules.12.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.12.updatedAt","allModules.13.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.13.createdAt","allModules.13.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.13.updatedAt","allModules.14.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.14.createdAt","allModules.14.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.14.updatedAt","allModules.15.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.15.createdAt","allModules.15.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.15.updatedAt","allModules.16.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.16.createdAt","allModules.16.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.16.updatedAt","allModules.17.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.17.createdAt","allModules.17.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.17.updatedAt","allModules.18.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.18.createdAt","allModules.18.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.18.updatedAt","allModules.19.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.19.createdAt","allModules.19.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.19.updatedAt","allModules.20.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.20.createdAt","allModules.20.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.20.updatedAt","allModules.21.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.21.createdAt","allModules.21.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.21.updatedAt","allModules.22.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.22.createdAt","allModules.22.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.22.updatedAt","allModules.23.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.23.createdAt","allModules.23.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.23.updatedAt","allModules.24.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.24.createdAt","allModules.24.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.24.updatedAt","allModules.25.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.25.createdAt","allModules.25.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.25.updatedAt","allModules.26.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.26.createdAt","allModules.26.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.26.updatedAt","allModules.27.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.27.createdAt","allModules.27.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.27.updatedAt","allModules.28.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.28.createdAt","allModules.28.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.28.updatedAt","allModules.29.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.29.createdAt","allModules.29.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.29.updatedAt","allModules.30.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.30.createdAt","allModules.30.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.30.updatedAt","allModules.31.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.31.createdAt","allModules.31.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.31.updatedAt","allModules.32.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.32.createdAt","allModules.32.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.32.updatedAt","allModules.33.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.33.createdAt","allModules.33.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.33.updatedAt","allModules.34.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.34.createdAt","allModules.34.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.34.updatedAt","allModules.35.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.35.createdAt","allModules.35.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.35.updatedAt","allModules.36.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.36.createdAt","allModules.36.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.36.updatedAt","allModules.37.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.37.createdAt","allModules.37.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.37.updatedAt","allModules.38.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.38.createdAt","allModules.38.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.38.updatedAt","allModules.39.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.39.createdAt","allModules.39.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.39.updatedAt","allModules.40.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.40.createdAt","allModules.40.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.40.updatedAt","allModules.41.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.41.createdAt","allModules.41.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.41.updatedAt","allModules.42.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.42.createdAt","allModules.42.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.42.updatedAt","allModules.43.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.43.createdAt","allModules.43.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.43.updatedAt","allModules.44.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.44.createdAt","allModules.44.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.44.updatedAt","allModules.45.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.45.createdAt","allModules.45.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.45.updatedAt","allModules.46.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.46.createdAt","allModules.46.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.46.updatedAt","allModules.47.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.47.createdAt","allModules.47.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.47.updatedAt","allModules.48.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.48.createdAt","allModules.48.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.48.updatedAt","allModules.49.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.49.createdAt","allModules.49.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.49.updatedAt","allModules.50.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.50.createdAt","allModules.50.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.50.updatedAt","allModules.51.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.51.createdAt","allModules.51.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.51.updatedAt","allModules.52.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.52.createdAt","allModules.52.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.52.updatedAt","allModules.53.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.53.createdAt","allModules.53.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.53.updatedAt","allModules.54.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.54.createdAt","allModules.54.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.54.updatedAt","allModules.55.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.55.createdAt","allModules.55.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.55.updatedAt","allModules.56.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.56.createdAt","allModules.56.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.56.updatedAt","allModules.57.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.57.createdAt","allModules.57.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.57.updatedAt","allModules.58.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.58.createdAt","allModules.58.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.58.updatedAt","allModules.59.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.59.createdAt","allModules.59.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.59.updatedAt","allModules.60.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.60.createdAt","allModules.60.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.60.updatedAt","allModules.61.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.61.createdAt","allModules.61.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.61.updatedAt","allModules.62.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.62.createdAt","allModules.62.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.62.updatedAt","allModules.63.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.63.createdAt","allModules.63.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.63.updatedAt","allModules.64.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.64.createdAt","allModules.64.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.64.updatedAt","allModules.65.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.65.createdAt","allModules.65.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.65.updatedAt","allModules.66.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.66.createdAt","allModules.66.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.66.updatedAt","allModules.67.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.67.createdAt","allModules.67.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.67.updatedAt","allModules.68.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.68.createdAt","allModules.68.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.68.updatedAt","allModules.69.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.69.createdAt","allModules.69.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.69.updatedAt","allModules.70.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.70.createdAt","allModules.70.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.70.updatedAt","allModules.71.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.71.createdAt","allModules.71.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.71.updatedAt","allModules.72.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.72.createdAt","allModules.72.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.72.updatedAt","allModules.73.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.73.createdAt","allModules.73.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.73.updatedAt","allModules.74.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.74.createdAt","allModules.74.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.74.updatedAt","allModules.75.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.75.createdAt","allModules.75.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.75.updatedAt","allModules.76.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.76.createdAt","allModules.76.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.76.updatedAt","allModules.77.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.77.createdAt","allModules.77.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.77.updatedAt","allModules.78.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.78.createdAt","allModules.78.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.78.updatedAt","allModules.79.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.79.createdAt","allModules.79.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.79.updatedAt","allModules.80.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.80.createdAt","allModules.80.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.80.updatedAt","allModules.81.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.81.createdAt","allModules.81.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.81.updatedAt","allModules.82.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.82.createdAt","allModules.82.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.82.updatedAt","allModules.83.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.83.createdAt","allModules.83.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.83.updatedAt","allModules.84.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.84.createdAt","allModules.84.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.84.updatedAt","allModules.85.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.85.createdAt","allModules.85.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.85.updatedAt","allModules.86.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.86.createdAt","allModules.86.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.86.updatedAt","allModules.87.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.87.createdAt","allModules.87.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.87.updatedAt","allModules.88.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.88.createdAt","allModules.88.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.88.updatedAt","allModules.89.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.89.createdAt","allModules.89.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.89.updatedAt","allModules.90.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.90.createdAt","allModules.90.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.90.updatedAt","allModules.91.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.91.createdAt","allModules.91.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.91.updatedAt","allModules.92.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.92.createdAt","allModules.92.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.92.updatedAt","allModules.93.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.93.createdAt","allModules.93.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.93.updatedAt","allModules.94.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.94.createdAt","allModules.94.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.94.updatedAt","allModules.95.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.95.createdAt","allModules.95.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.95.updatedAt","allModules.96.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.96.createdAt","allModules.96.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.96.updatedAt","allModules.97.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.97.createdAt","allModules.97.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.97.updatedAt","allModules.98.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.98.createdAt","allModules.98.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.98.updatedAt","allModules.99.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.99.createdAt","allModules.99.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.99.updatedAt","allModules.100.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.100.createdAt","allModules.100.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.100.updatedAt","allModules.101.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.101.createdAt","allModules.101.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.101.updatedAt","allModules.102.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.102.createdAt","allModules.102.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.102.updatedAt","allModules.103.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.103.createdAt","allModules.103.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.103.updatedAt","allModules.104.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.104.createdAt","allModules.104.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.104.updatedAt","allModules.105.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.105.createdAt","allModules.105.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.105.updatedAt","allModules.106.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.106.createdAt","allModules.106.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.106.updatedAt","allModules.107.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.107.createdAt","allModules.107.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.107.updatedAt","allModules.108.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.108.createdAt","allModules.108.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.108.updatedAt","allModules.109.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.109.createdAt","allModules.109.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.109.updatedAt","allModules.110.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.110.createdAt","allModules.110.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.110.updatedAt","allModules.111.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.111.createdAt","allModules.111.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.111.updatedAt","allModules.112.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.112.createdAt","allModules.112.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.112.updatedAt","allModules.113.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.113.createdAt","allModules.113.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.113.updatedAt","allModules.114.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.114.createdAt","allModules.114.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.114.updatedAt","allModules.115.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.115.createdAt","allModules.115.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.115.updatedAt","allModules.116.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.116.createdAt","allModules.116.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.116.updatedAt","allModules.117.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.117.createdAt","allModules.117.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.117.updatedAt","allModules.118.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.118.createdAt","allModules.118.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.118.updatedAt","allModules.119.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.119.createdAt","allModules.119.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.119.updatedAt","allModules.120.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.120.createdAt","allModules.120.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.120.updatedAt","allModules.121.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.121.createdAt","allModules.121.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.121.updatedAt","allModules.122.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.122.createdAt","allModules.122.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.122.updatedAt","allModules.123.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.123.createdAt","allModules.123.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.123.updatedAt","allModules.124.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.124.createdAt","allModules.124.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.124.updatedAt","allModules.125.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.125.createdAt","allModules.125.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.125.updatedAt","allModules.126.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.126.createdAt","allModules.126.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.126.updatedAt","allModules.127.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.127.createdAt","allModules.127.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.127.updatedAt","allModules.128.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.128.createdAt","allModules.128.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.128.updatedAt","allModules.129.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.129.createdAt","allModules.129.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.129.updatedAt","allModules.130.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.130.createdAt","allModules.130.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.130.updatedAt","allModules.131.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.131.createdAt","allModules.131.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.131.updatedAt","allModules.132.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.132.createdAt","allModules.132.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.132.updatedAt","allModules.133.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.133.createdAt","allModules.133.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.133.updatedAt","allModules.134.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.134.createdAt","allModules.134.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.134.updatedAt","allModules.135.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.135.createdAt","allModules.135.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.135.updatedAt","allModules.136.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.136.createdAt","allModules.136.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.136.updatedAt","allModules.137.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.137.createdAt","allModules.137.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.137.updatedAt","allModules.138.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.138.createdAt","allModules.138.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.138.updatedAt","allModules.139.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.139.createdAt","allModules.139.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.139.updatedAt","allModules.140.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.140.createdAt","allModules.140.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.140.updatedAt","allModules.141.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.141.createdAt","allModules.141.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.141.updatedAt","allModules.142.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.142.createdAt","allModules.142.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.142.updatedAt","allModules.143.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.143.createdAt","allModules.143.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.143.updatedAt","allModules.144.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.144.createdAt","allModules.144.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.144.updatedAt","allModules.145.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.145.createdAt","allModules.145.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.145.updatedAt","allModules.146.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.146.createdAt","allModules.146.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.146.updatedAt","allModules.147.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.147.createdAt","allModules.147.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.147.updatedAt","allModules.148.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.148.createdAt","allModules.148.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.148.updatedAt","allModules.149.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.149.createdAt","allModules.149.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.149.updatedAt","allModules.150.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.150.createdAt","allModules.150.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.150.updatedAt","allModules.151.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.151.createdAt","allModules.151.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.151.updatedAt","allModules.152.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.152.createdAt","allModules.152.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.152.updatedAt","allModules.153.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.153.createdAt","allModules.153.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.153.updatedAt","allModules.154.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.154.createdAt","allModules.154.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.154.updatedAt","allModules.155.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.155.createdAt","allModules.155.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.155.updatedAt","allModules.156.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.156.createdAt","allModules.156.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.156.updatedAt","allModules.157.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.157.createdAt","allModules.157.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.157.updatedAt","allModules.158.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.158.createdAt","allModules.158.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.158.updatedAt","allModules.159.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.159.createdAt","allModules.159.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.159.updatedAt","allModules.160.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.160.createdAt","allModules.160.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.160.updatedAt","allModules.161.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.161.createdAt","allModules.161.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.161.updatedAt","allModules.162.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.162.createdAt","allModules.162.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.162.updatedAt","allModules.163.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.163.createdAt","allModules.163.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.163.updatedAt","allModules.164.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.164.createdAt","allModules.164.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.164.updatedAt","allModules.165.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.165.createdAt","allModules.165.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.165.updatedAt","allModules.166.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.166.createdAt","allModules.166.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.166.updatedAt","allModules.167.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.167.createdAt","allModules.167.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.167.updatedAt","allModules.168.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.168.createdAt","allModules.168.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.168.updatedAt","allModules.169.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.169.createdAt","allModules.169.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.169.updatedAt","allModules.170.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.170.createdAt","allModules.170.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.170.updatedAt","allModules.171.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.171.createdAt","allModules.171.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.171.updatedAt","allModules.172.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.172.createdAt","allModules.172.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.172.updatedAt","allModules.173.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.173.createdAt","allModules.173.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.173.updatedAt","allModules.174.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.174.createdAt","allModules.174.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.174.updatedAt","allModules.175.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.175.createdAt","allModules.175.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.175.updatedAt","allModules.176.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.176.createdAt","allModules.176.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.176.updatedAt","allModules.177.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.177.createdAt","allModules.177.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.177.updatedAt","allModules.178.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.178.createdAt","allModules.178.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.178.updatedAt","allModules.179.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.179.createdAt","allModules.179.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.179.updatedAt","allModules.180.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.180.createdAt","allModules.180.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.180.updatedAt","allModules.181.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.181.createdAt","allModules.181.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.181.updatedAt","allModules.182.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.182.createdAt","allModules.182.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.182.updatedAt","allModules.183.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.183.createdAt","allModules.183.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.183.updatedAt","allModules.184.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.184.createdAt","allModules.184.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.184.updatedAt","allModules.185.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.185.createdAt","allModules.185.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.185.updatedAt","allModules.186.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.186.createdAt","allModules.186.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.186.updatedAt","allModules.187.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.187.createdAt","allModules.187.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.187.updatedAt","allModules.188.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.188.createdAt","allModules.188.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.188.updatedAt","allModules.189.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.189.createdAt","allModules.189.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.189.updatedAt","allModules.190.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.190.createdAt","allModules.190.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.190.updatedAt","allModules.191.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.191.createdAt","allModules.191.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.191.updatedAt","allModules.192.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.192.createdAt","allModules.192.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.192.updatedAt","allModules.193.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.193.createdAt","allModules.193.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.193.updatedAt","allModules.194.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.194.createdAt","allModules.194.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.194.updatedAt","allModules.195.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.195.createdAt","allModules.195.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.195.updatedAt","allModules.196.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.196.createdAt","allModules.196.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.196.updatedAt","allModules.197.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.197.createdAt","allModules.197.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.197.updatedAt","allModules.198.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.198.createdAt","allModules.198.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.198.updatedAt","allModules.199.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.199.createdAt","allModules.199.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.199.updatedAt","allModules.200.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.200.createdAt","allModules.200.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.200.updatedAt","allModules.201.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.201.createdAt","allModules.201.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.201.updatedAt","allModules.202.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.202.createdAt","allModules.202.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.202.updatedAt","allModules.203.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.203.createdAt","allModules.203.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.203.updatedAt","allModules.204.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.204.createdAt","allModules.204.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.204.updatedAt","allModules.205.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.205.createdAt","allModules.205.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.205.updatedAt","allModules.206.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.206.createdAt","allModules.206.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.206.updatedAt","allModules.207.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.207.createdAt","allModules.207.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.207.updatedAt","allModules.208.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.208.createdAt","allModules.208.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.208.updatedAt","allModules.209.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.209.createdAt","allModules.209.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.209.updatedAt","allModules.210.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.210.createdAt","allModules.210.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.210.updatedAt","allModules.211.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.211.createdAt","allModules.211.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.211.updatedAt","allModules.212.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.212.createdAt","allModules.212.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.212.updatedAt","allModules.213.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.213.createdAt","allModules.213.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.213.updatedAt","allModules.214.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.214.createdAt","allModules.214.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.214.updatedAt","allModules.215.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.215.createdAt","allModules.215.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.215.updatedAt","allModules.216.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.216.createdAt","allModules.216.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.216.updatedAt","allModules.217.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.217.createdAt","allModules.217.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.217.updatedAt","allModules.218.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.218.createdAt","allModules.218.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.218.updatedAt","allModules.219.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.219.createdAt","allModules.219.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.219.updatedAt","allModules.220.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.220.createdAt","allModules.220.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.220.updatedAt","allModules.221.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.221.createdAt","allModules.221.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.221.updatedAt","allModules.222.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.222.createdAt","allModules.222.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.222.updatedAt","allModules.223.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.223.createdAt","allModules.223.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.223.updatedAt","allModules.224.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.224.createdAt","allModules.224.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.224.updatedAt","allModules.225.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.225.createdAt","allModules.225.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.225.updatedAt","allModules.226.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.226.createdAt","allModules.226.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.226.updatedAt","allModules.227.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.227.createdAt","allModules.227.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.227.updatedAt","allModules.228.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.228.createdAt","allModules.228.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.228.updatedAt","allModules.229.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.229.createdAt","allModules.229.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.229.updatedAt","allModules.230.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.230.createdAt","allModules.230.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.230.updatedAt","allModules.231.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.231.createdAt","allModules.231.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.231.updatedAt","allModules.232.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.232.createdAt","allModules.232.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.232.updatedAt","allModules.233.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.233.createdAt","allModules.233.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.233.updatedAt","allModules.234.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.234.createdAt","allModules.234.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.234.updatedAt","allModules.235.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.235.createdAt","allModules.235.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.235.updatedAt","allModules.236.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.236.createdAt","allModules.236.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.236.updatedAt","allModules.237.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.237.createdAt","allModules.237.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.237.updatedAt","allModules.238.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.238.createdAt","allModules.238.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.238.updatedAt","allModules.239.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.239.createdAt","allModules.239.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.239.updatedAt","allModules.240.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.240.createdAt","allModules.240.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.240.updatedAt","allModules.241.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.241.createdAt","allModules.241.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.241.updatedAt","allModules.242.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.242.createdAt","allModules.242.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.242.updatedAt","allModules.243.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.243.createdAt","allModules.243.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.243.updatedAt","allModules.244.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.244.createdAt","allModules.244.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.244.updatedAt","allModules.245.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.245.createdAt","allModules.245.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.245.updatedAt","allModules.246.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.246.createdAt","allModules.246.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.246.updatedAt","allModules.247.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.247.createdAt","allModules.247.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.247.updatedAt","allModules.248.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.248.createdAt","allModules.248.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.248.updatedAt","allModules.249.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.249.createdAt","allModules.249.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.249.updatedAt","allModules.250.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.250.createdAt","allModules.250.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.250.updatedAt","allModules.251.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.251.createdAt","allModules.251.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.251.updatedAt","allModules.252.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.252.createdAt","allModules.252.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.252.updatedAt","allModules.253.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.253.createdAt","allModules.253.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.253.updatedAt","allModules.254.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.254.createdAt","allModules.254.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.254.updatedAt","allModules.255.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.255.createdAt","allModules.255.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.255.updatedAt","allModules.256.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.256.createdAt","allModules.256.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.256.updatedAt","allModules.257.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.257.createdAt","allModules.257.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.257.updatedAt","allModules.258.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.258.createdAt","allModules.258.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.258.updatedAt","allModules.259.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.259.createdAt","allModules.259.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.259.updatedAt","allModules.260.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.260.createdAt","allModules.260.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.260.updatedAt","allModules.261.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.261.createdAt","allModules.261.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.261.updatedAt","allModules.262.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.262.createdAt","allModules.262.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.262.updatedAt","allModules.263.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.263.createdAt","allModules.263.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.263.updatedAt","allModules.264.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.264.createdAt","allModules.264.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.264.updatedAt","allModules.265.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.265.createdAt","allModules.265.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.265.updatedAt","allModules.266.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.266.createdAt","allModules.266.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.266.updatedAt","allModules.267.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.267.createdAt","allModules.267.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.267.updatedAt","allModules.268.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.268.createdAt","allModules.268.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.268.updatedAt","allModules.269.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.269.createdAt","allModules.269.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.269.updatedAt","allModules.270.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.270.createdAt","allModules.270.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.270.updatedAt","allModules.271.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.271.createdAt","allModules.271.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.271.updatedAt","allModules.272.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.272.createdAt","allModules.272.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.272.updatedAt","allModules.273.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.273.createdAt","allModules.273.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.273.updatedAt","allModules.274.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.274.createdAt","allModules.274.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.274.updatedAt","allModules.275.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.275.createdAt","allModules.275.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.275.updatedAt","allModules.276.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.276.createdAt","allModules.276.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.276.updatedAt","allModules.277.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.277.createdAt","allModules.277.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.277.updatedAt","allModules.278.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.278.createdAt","allModules.278.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.278.updatedAt","allModules.279.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.279.createdAt","allModules.279.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.279.updatedAt","allModules.280.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.280.createdAt","allModules.280.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.280.updatedAt","allModules.281.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.281.createdAt","allModules.281.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.281.updatedAt","allModules.282.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.282.createdAt","allModules.282.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.282.updatedAt","allModules.283.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.283.createdAt","allModules.283.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.283.updatedAt","allModules.284.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.284.createdAt","allModules.284.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.284.updatedAt","allModules.285.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.285.createdAt","allModules.285.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.285.updatedAt","allModules.286.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.286.createdAt","allModules.286.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.286.updatedAt","allModules.287.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.287.createdAt","allModules.287.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.287.updatedAt","allModules.288.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.288.createdAt","allModules.288.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.288.updatedAt","allModules.289.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.289.createdAt","allModules.289.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.289.updatedAt","allModules.290.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.290.createdAt","allModules.290.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.290.updatedAt","allModules.291.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.291.createdAt","allModules.291.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.291.updatedAt","allModules.292.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.292.createdAt","allModules.292.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.292.updatedAt","allModules.293.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.293.createdAt","allModules.293.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.293.updatedAt","allModules.294.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.294.createdAt","allModules.294.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.294.updatedAt","allModules.295.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.295.createdAt","allModules.295.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.295.updatedAt","allModules.296.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.296.createdAt","allModules.296.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.296.updatedAt","allModules.297.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.297.createdAt","allModules.297.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.297.updatedAt","allModules.298.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.298.createdAt","allModules.298.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.298.updatedAt","allModules.299.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.299.createdAt","allModules.299.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.299.updatedAt","allModules.300.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.300.createdAt","allModules.300.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.300.updatedAt","allModules.301.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.301.createdAt","allModules.301.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.301.updatedAt","allModules.302.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.302.createdAt","allModules.302.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.302.updatedAt","allModules.303.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.303.createdAt","allModules.303.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.303.updatedAt","allModules.304.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.304.createdAt","allModules.304.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.304.updatedAt","allModules.305.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.305.createdAt","allModules.305.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.305.updatedAt","allModules.306.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.306.createdAt","allModules.306.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.306.updatedAt","allModules.307.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.307.createdAt","allModules.307.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.307.updatedAt","allModules.308.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.308.createdAt","allModules.308.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.308.updatedAt","allModules.309.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.309.createdAt","allModules.309.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.309.updatedAt","allModules.310.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.310.createdAt","allModules.310.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.310.updatedAt","allModules.311.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.311.createdAt","allModules.311.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.311.updatedAt","allModules.312.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.312.createdAt","allModules.312.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.312.updatedAt","allModules.313.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.313.createdAt","allModules.313.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.313.updatedAt","allModules.314.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.314.createdAt","allModules.314.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.314.updatedAt","allModules.315.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.315.createdAt","allModules.315.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.315.updatedAt","allModules.316.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.316.createdAt","allModules.316.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.316.updatedAt","allModules.317.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.317.createdAt","allModules.317.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.317.updatedAt","allModules.318.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.318.createdAt","allModules.318.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.318.updatedAt","allModules.319.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.319.createdAt","allModules.319.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.319.updatedAt","allModules.320.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.320.createdAt","allModules.320.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.320.updatedAt","allModules.321.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.321.createdAt","allModules.321.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.321.updatedAt","allModules.322.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.322.createdAt","allModules.322.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.322.updatedAt","allModules.323.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.323.createdAt","allModules.323.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.323.updatedAt","allModules.324.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.324.createdAt","allModules.324.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.324.updatedAt","allModules.325.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.325.createdAt","allModules.325.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.325.updatedAt","allModules.326.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.326.createdAt","allModules.326.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.326.updatedAt","allModules.327.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.327.createdAt","allModules.327.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.327.updatedAt"},"referentialEqualities":{"allModules":["path.modules"],"prevModule":["path.modules.315"],"currentModule":["path.modules.316"],"nextModule":["path.modules.317"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792304068465,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningPaths","getModule"],{"input":{"pathSlug":"machine-learning","contentType":"LEARNING","contentSlug":"ml-project-management"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getModule\"],{\"input\":{\"contentSlug\":\"ml-project-management\",\"contentType\":\"LEARNING\",\"pathSlug\":\"machine-learning\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068681,"state":{"data":{"json":{"id":"seed_122ddb8ec53db2593f212b7fff918ecd","slug":"ml-project-management","category":"Machine Learning","topic":"ML Project Management","title":"ML Project Management","description":"Master the systematic approach to managing machine learning projects from inception to deployment. Learn problem scoping, data collection planning, experiment management, iteration strategies, and launch criteria that separate successful ML initiatives from failed ones.","difficulty":"ADVANCED","estimatedMinutes":90,"pages":[{"id":"seed_877ea1e28d3391ee7bbb06ebedda76b6","title":"Problem Scoping","order":0,"content":[{"id":"opening-hook","title":"The Foundation of ML Success","content":[{"data":{"content":"In the landscape of machine learning failures, a striking pattern emerges: **the majority of ML projects fail not because of algorithmic limitations or data quality issues, but because the problem was poorly defined from the outset**. Industry surveys consistently report that 60-80% of ML projects never reach production, and the root cause often traces back to the earliest phase—problem scoping.\n\nProblem scoping is the disciplined practice of translating ambiguous business needs into precisely formulated machine learning problems. It encompasses understanding stakeholder objectives, assessing technical feasibility, defining success metrics, and establishing the boundaries of what ML can and cannot deliver. It is simultaneously the most underrated and most consequential phase of any ML project.\n\nThis page provides a comprehensive framework for problem scoping that distinguishes successful ML practitioners from those who build sophisticated solutions to the wrong problems."},"type":"text"},{"data":{"icon":"Target","title":"What You Will Master","content":"By completing this page, you will be able to: (1) Translate vague business requests into precisely formulated ML problems, (2) Assess whether ML is the appropriate solution for a given problem, (3) Define measurable success criteria that align with business objectives, (4) Identify and mitigate common scoping pitfalls, and (5) Communicate technical feasibility to non-technical stakeholders with clarity and precision.","variant":"info"},"type":"callout"}]},{"id":"business-context","title":"Understanding the Business Context","content":[{"data":{"content":"Every ML project begins with a business need, but stakeholders rarely articulate that need in ML-compatible terms. A product manager might say, *\"We want to reduce customer churn,\"* while a finance director requests *\"better fraud detection.\"* These statements describe outcomes, not problems. The ML practitioner's first task is to excavate the underlying structure.\n\n**The Translation Problem:**\n\nConsider the gap between business language and ML formulation:"},"type":"text"},{"data":{"rows":[["\"Reduce churn\"","When is churn measured? What actions are possible?","Binary classification: Will user churn in next X days?"],["\"Better recommendations\"","Better how? Engagement? Revenue? Diversity?","Multi-objective ranking with explicit tradeoffs"],["\"Automate support\"","What fraction? Edge case handling?","Multi-class classification + confidence thresholding"],["\"Detect anomalies\"","What constitutes an anomaly? False positive tolerance?","Unsupervised detection with domain-specific thresholds"]],"headers":["Business Ask","Hidden Complexity","ML Formulation Required"]},"type":"table"},{"data":{"content":"The translation process requires extensive stakeholder dialogue, not just surface-level requirements gathering."},"type":"text"},{"data":{"items":["**What is the current process?** — How is this task performed today? Manual? Rule-based? Understanding the baseline reveals what ML must beat and exposes constraints.","**What decisions will this model drive?** — The model output must be actionable. A churn prediction is useless without a retention intervention mechanism.","**What are the costs of different errors?** — False positives vs. false negatives have vastly different business implications. Medical diagnosis ≠ spam filtering.","**What is the latency constraint?** — Real-time inference requires different architectures than batch processing. This affects model complexity choices.","**Who are the end users?** — Model consumers shape output format. An API for developers differs from a dashboard for analysts differs from risk scores for underwriters."],"title":{"icon":"Target","text":"Critical Questions for Business Context Discovery","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"AlertCircle","title":"The Stakeholder Alignment Trap","content":"Different stakeholders often have conflicting definitions of success. Marketing wants engagement; Finance wants revenue; Trust & Safety wants risk reduction. Failing to surface and resolve these conflicts early leads to models that satisfy no one. Explicit prioritization conversations are mandatory.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"feasibility-assessment","title":"The Feasibility Assessment Framework","content":[{"data":{"content":"$72"},"type":"text"},{"data":{"left":[{"data":{"items":["Historical labels exist and are reliable","Human experts can perform the task (signal exists)","Data volume is sufficient for task complexity","Clear, measurable success metrics are defined","Latency and infrastructure constraints are compatible","The decision boundary is learnable, not chaotic"],"title":{"icon":"CheckCircle","text":"Green Flags: Proceed with ML","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Labels don't exist or require expensive creation","Even human experts cannot perform the task reliably","Fewer than thousands of labeled examples available","Success metrics are subjective or political","Required accuracy exceeds state-of-the-art","Data distribution will shift rapidly post-deployment"],"title":{"icon":"XCircle","text":"Red Flags: Reconsider ML Approach","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"content":"$73"},"type":"text"},{"data":{"icon":"Lightbulb","title":"The 10x Rule of Thumb","content":"For an ML project to justify its complexity, it should deliver at least 10x the value of a simpler alternative. If a rule-based system achieves 85% of the benefit at 10% of the cost, that's often the right choice. ML should be reserved for problems where the performance gap is substantial and economically meaningful.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"formulating-ml-problem","title":"Formulating the ML Problem","content":[{"data":{"content":"Once feasibility is established, the next challenge is precise problem formulation. This is where business objectives become mathematical specifications. The formulation defines:\n\n1. **Task type** — Classification, regression, ranking, generation, etc.\n2. **Input specification** — What features are available at inference time?\n3. **Output specification** — What exactly does the model produce?\n4. **Objective function** — What does the model optimize?\n5. **Evaluation metrics** — How is model quality assessed?\n\n**Task Type Selection:**\n\nThe same business problem can often be formulated as different ML tasks. Consider \"predicting customer lifetime value\":"},"type":"text"},{"data":{"rows":[["Exact LTV prediction","Regression","Dollar amount","High variance, requires revenue history"],["LTV tier classification","Multi-class","High/Medium/Low","Lower granularity, more robust"],["High-value customer detection","Binary","Yes/No","Simplest, loses LTV ordering"],["LTV percentile ranking","Learning to Rank","Relative ordering","No absolute values, easier calibration"]],"headers":["Formulation","Task Type","Output","Tradeoffs"]},"type":"table"},{"data":{"content":"Each formulation has different data requirements, modeling approaches, and business implications. The choice should be driven by how the output will be used, not modeling convenience."},"type":"text"},{"data":{"code":"$74","fileName":"problem_formulation_template.py","languages":[{"label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"content":"**The Critical Role of Label Definition:**\n\nThe single most consequential decision in problem formulation is the **label definition**. Ambiguity here propagates through every subsequent step:\n\n*Consider \"fraud detection\":*\n- Is fraud defined by confirmed investigations? (Precision-focused but misses soft fraud)\n- By chargebacks? (Includes friendly fraud, misses non-disputed fraud)\n- By rule-based triggers? (Learns to mimic rules, not detect fraud)\n- By model predictions? (Circular definition disaster)\n\nEach definition produces a different training set, optimizes for different behavior, and has different performance characteristics. **There is no objectively correct definition—only business-appropriate definitions.** The choice must be explicit and documented."},"type":"text"},{"data":{"icon":"AlertCircle","title":"Proxy Metric Peril","content":"When the true objective cannot be directly measured, we use proxies. But proxies create gaps. Optimizing for click-through rate as a proxy for user satisfaction produces clickbait. Optimizing for time-on-site as a proxy for engagement produces addiction loops. Always document the gap between your measurable proxy and the true business objective, and implement guardrails against proxy gaming.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"scope-boundaries","title":"Scope Boundaries and Constraints","content":[{"data":{"content":"$75"},"type":"text"},{"data":{"rows":[["Latency","< 50ms P99","Limits model complexity, may require distillation"],["Throughput","10K inferences/second","Batch processing, GPU requirements"],["Memory","< 500MB model size","Quantization, pruning, smaller architectures"],["Cost","$$X per 1000 inferences","Simpler models, caching strategies"],["Privacy","No PII in features","Feature engineering constraints, federated approaches"],["Explainability","Regulatory explanations required","Interpretable models or post-hoc explanations"],["Availability","99.9% uptime","Fallback mechanisms, graceful degradation"],["Freshness","Updates within 1 hour","Streaming pipelines, incremental learning"]],"caption":"ML System Constraint Categories","headers":["Constraint Type","Examples","Impact on Solution"]},"type":"table"},{"data":{"content":"$76"},"type":"text"},{"data":{"icon":"Lightbulb","title":"The One-Pager Rule","content":"A well-scoped ML problem can be completely described in a single page. If your problem statement requires extensive documentation to be understood, it's either too complex (break it down) or too vague (add specificity). The one-pager forces clarity and enables rapid alignment across stakeholders.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"success-metrics","title":"Success Metrics and Evaluation Criteria","content":[{"data":{"content":"Before any modeling begins, success must be defined in measurable terms. This requires distinguishing between **model metrics** (how the model performs technically) and **business metrics** (impact on business outcomes).\n\n**Model Metrics (Offline Evaluation):**\n\nThese are the traditional ML performance measures computed on held-out test sets:"},"type":"text"},{"data":{"rows":[["Binary Classification","AUC-ROC, Precision@K, Recall@K, F1","AUC insensitive to class imbalance; choose based on decision threshold"],["Multi-class","Macro/Micro F1, Confusion Matrix, Top-K Accuracy","Class weights matter for imbalanced classes"],["Regression","MAE, RMSE, MAPE, R²","RMSE penalizes large errors; MAE more robust to outliers"],["Ranking","NDCG, MRR, MAP","Position-weighted; critical for recommendation systems"],["Calibration","Brier Score, ECE","Essential when probabilities drive decisions"]],"headers":["Task Type","Common Metrics","Considerations"]},"type":"table"},{"data":{"content":"**Business Metrics (Online Evaluation):**\n\nThese connect model performance to business value:"},"type":"text"},{"data":{"rows":[["Revenue impact","Incremental revenue from model decisions","Model precision → Correct targeting → Revenue"],["Cost reduction","Savings from automation vs. manual","Model coverage × Accuracy → Reduced manual review"],["User engagement","Downstream user behavior changes","Recommendation quality → User satisfaction → Engagement"],["Risk reduction","Prevented losses from adverse events","Detection rate × True precision → Prevented fraud"]],"headers":["Business Metric","Definition","Connection to Model"]},"type":"table"},{"data":{"content":"**The Metric Selection Process:**\n\nChoosing the right primary metric requires understanding the decision-making context:\n\n1. **Identify the operating point** — Where on the precision-recall curve will the system operate?\n - High-stakes, limited capacity (e.g., manual fraud review): Optimize precision at low recall\n - Low-stakes, broad coverage (e.g., email spam): Optimize recall at acceptable precision\n\n2. **Match metric to business cost** — Translate error types to dollar values\n - False positive cost: Manual review cost + customer friction\n - False negative cost: Actual loss from missed detection\n - The ratio of these costs determines the optimal threshold\n\n3. **Consider calibration requirements** — If probabilities are used directly in downstream calculations (pricing, risk scoring), calibration metrics are essential\n\n4. **Account for distribution shift** — Metrics that are robust to distribution changes (normalized metrics, rank-based metrics) may be preferable in volatile domains"},"type":"text"},{"data":{"code":"$77","fileName":"metric_framework.py","languages":[{"label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Info","title":"The Offline-Online Gap","content":"Offline model metrics often don't translate directly to online business impact. A model with 5% higher AUC may produce only 1% business improvement—or 20% improvement—depending on the decision-making context. Always plan for online experimentation (A/B tests) to measure true business impact, and treat offline metrics as necessary but not sufficient conditions for success.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"scoping-pitfalls","title":"Common Scoping Pitfalls and How to Avoid Them","content":[{"data":{"content":"$78"},"type":"text"},{"data":{"items":["**The Perfect Label Myth** — Assuming labels are clean and consistent. In reality, labeling is noisy, subjective, and expensive. Scope must account for label acquisition and quality.","**Ignoring Data Availability** — Scoping a model that requires features that don't exist or can't be computed at inference time. Always verify data availability early.","**Optimistic Timeline Bias** — ML projects routinely take 2-5x longer than estimated. Scope should include buffer for experimentation, debugging, and iteration.","**The Production Gap** — Scoping model development without considering serving infrastructure, monitoring, or maintenance. A model without deployment is a science project.","**Stakeholder Telephone** — Receiving requirements through intermediaries who don't fully understand the technical implications. Always speak directly with decision-makers.","**Sunk Cost Commitment** — Refusing to descope or cancel a project that becomes infeasible. Killing bad projects early is a feature, not a failure."],"title":{"icon":"AlertCircle","text":"Critical Scoping Anti-Patterns","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"content":"$79"},"type":"text"},{"data":{"icon":"AlertCircle","title":"The Maintenance Blind Spot","content":"Scoping typically focuses on building the model, but 80% of ML cost is in maintenance. Distribution shift, data pipeline failures, model degradation, and retraining cycles are predictable. A scoping document that doesn't address ongoing maintenance is incomplete. Include: Who monitors? How often is retraining? What triggers retraining? What are the fallback mechanisms?","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"scoping-document","title":"The ML Scoping Document Template","content":[{"data":{"content":"$7a"},"type":"text"},{"data":{"code":"$7b","fileName":"scoping_document.md","languages":[{"label":"Markdown","value":"markdown"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Lightbulb","title":"The Living Document","content":"The scoping document should be a living document, updated as new information emerges. However, changes after the scoping phase require explicit stakeholder agreement. The document serves as a change control mechanism—preventing silent scope expansion while allowing deliberate scope evolution.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"summary","title":"Summary and Key Takeaways","content":[{"data":{"content":"Problem scoping is the foundation upon which successful ML projects are built. The time invested in rigorous scoping pays dividends throughout the project lifecycle—reducing wasted effort, aligning stakeholders, and ensuring that the model you build is the model the business needs."},"type":"text"},{"data":{"type":"ordered","items":["**Translate business language to ML formulation** — The stakeholder's ask is rarely the ML problem. Deep discovery is required to extract the underlying structure.","**Assess feasibility across all dimensions** — Data, technical, organizational, and economic feasibility must all be satisfied. One failure mode kills the project.","**Define labels with extreme precision** — Label definition is the most consequential scoping decision. Ambiguity here corrupts everything downstream.","**Establish explicit boundaries** — What's in scope, what's out, what assumptions are made. Scope creep is the enemy of delivery.","**Separate model metrics from business metrics** — Offline model performance is necessary but not sufficient. Plan for online experimentation.","**Document everything** — The scoping document is a contract. It protects the team and aligns stakeholders.","**Plan for maintenance** — ML systems require ongoing care. Scope must include monitoring, retraining, and maintenance capacity."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's Next:**\n\nWith a well-scoped problem in hand, the next critical phase is data collection planning. The next page examines how to systematically plan data acquisition, ensure data quality, and build data pipelines that support both model development and production operation."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand the systematic discipline of ML problem scoping. This capability separates ML practitioners who ship meaningful products from those who build impressive models that never reach production. Next, we'll explore data collection planning—the fuel that powers every ML system.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_ef3cfcdc33e9e1e026cfd75d00380c8f","title":"Data Collection Planning","order":1,"content":null,"isLocked":true},{"id":"seed_25075dce25ecddadca46f876a40d36f0","title":"Experiment Management","order":2,"content":null,"isLocked":true},{"id":"seed_9e6bfb02646e5dca43d4dcc3111e7182","title":"Iteration Strategies","order":3,"content":null,"isLocked":true},{"id":"seed_4ff1ad1d9656b082b07548425a47280a","title":"Launch Criteria","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792304068633,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"ml-project-management"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"ml-project-management\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068681,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304068634,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_122ddb8ec53db2593f212b7fff918ecd"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_122ddb8ec53db2593f212b7fff918ecd\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068681,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304068634,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","get"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_4ff1ad1d9656b082b07548425a47280a"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_4ff1ad1d9656b082b07548425a47280a\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068681,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304068634,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","getUserLike"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_4ff1ad1d9656b082b07548425a47280a"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_4ff1ad1d9656b082b07548425a47280a\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068681,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792304068634,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["highlights","getHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_4ff1ad1d9656b082b07548425a47280a"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_4ff1ad1d9656b082b07548425a47280a\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304068682,"state":{"data":"$undefined","dataUpdateCount":0,"dataUpdatedAt":0,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"pending","fetchStatus":"fetching"},"queryKey":[["highlights","getPopularHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_4ff1ad1d9656b082b07548425a47280a","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_4ff1ad1d9656b082b07548425a47280a\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@7c"}]},"children":"$L7d"}]