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:T44d,In the era of deep neural networks and complex ensemble methods, it's easy to forget that one of the most powerful tools in machine learning remains fundamentally transparent: **linear models**. When someone asks 'how does your model work?', a linear model provides a complete, mathematically precise answer through its coefficients. Linear models—including linear regression, logistic regression, and their regularized variants—offer **intrinsic interpretability**. Unlike black-box models that require post-hoc explanation methods, linear models wear their decision-making logic on their sleeve. Each coefficient directly tells you how much each feature contributes to the prediction. But this apparent simplicity is deceptive. Correctly interpreting linear model coefficients requires deep understanding of standardization, multicollinearity, regularization effects, causal reasoning, and the distinction between statistical significance and practical importance. Misinterpreting coefficients leads to flawed conclusions, incorrect interventions, and broken trust in machine learning systems.73:T4fb,import numpy as np from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.preprocessing import StandardScaler # Example: Linear Regression Interpretation X = np.array([[1500, 3, 20], [2000, 4, 15], [1200, 2, 30], [1800, 3, 10]]) y = np.array([300000, 450000, 200000, 400000]) feature_names = ['sqft', 'bedrooms', 'age'] lr = LinearRegression() lr.fit(X, y) print("Linear Regression Coefficients:") print(f"Intercept: ${lr.intercept_:, .2f}") for name, coef in zip(feature_names, lr.coef_): print(f" {name}: ${coef:,.2f} per unit increase") # Example: Logistic Regression - Odds Ratio Interpretation X_binary = np.array([[25, 50000], [35, 80000], [45, 60000], [30, 100000]]) y_binary = np.array([0, 1, 0, 1]) # Purchased premium product log_reg = LogisticRegression() log_reg.fit(X_binary, y_binary) print(" Logistic Regression Coefficients (Log-Odds):") for name, coef in zip(['age', 'income'], log_reg.coef_[0]): print(f" {name}: {coef:.4f}") print(f" Odds Ratio: {np.exp(coef):.4f}") # Interpretation for income: # If odds ratio = 1.00005, then each $1 increase in income # multiplies the odds of purchase by 1.00005 # For $10,000 increase: 1.00005 ^ 10000 ≈ 1.65(65 % increase in odds)74:T586,import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler # Simulate loan default data np.random.seed(42) n = 1000 income = np.random.uniform(20000, 500000, n) credit_score = np.random.uniform(300, 850, n) dti_ratio = np.random.uniform(0, 2, n) # True relationship(with different scales) default_prob = -income * 0.00001 - credit_score * 0.008 + dti_ratio * 1.5 y = (default_prob > np.random.normal(0, 1, n)).astype(int) X = np.column_stack([income, credit_score, dti_ratio]) feature_names = ['income', 'credit_score', 'dti_ratio'] # Fit on raw data lr_raw = LinearRegression() lr_raw.fit(X, y) print("Raw Coefficients:") for name, coef in zip(feature_names, lr_raw.coef_): print(f" {name}: {coef:.8f}") # Fit on standardized data scaler = StandardScaler() X_std = scaler.fit_transform(X) lr_std = LinearRegression() lr_std.fit(X_std, y) print(" Standardized Coefficients:") for name, coef in zip(feature_names, lr_std.coef_): print(f" {name}: {coef:.4f}") # Alternative: compute standardized coefficients from raw print(" Standardized from Raw (verification):") for name, coef, scale in zip(feature_names, lr_raw.coef_, scaler.scale_): std_coef = coef * scale # Convert to standardized print(f" {name}: {std_coef:.4f}")75:T60b,import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression from statsmodels.stats.outliers_influence import variance_inflation_factor # Create data with multicollinearity np.random.seed(42) n = 500 # sqft and num_rooms are highly correlated sqft = np.random.uniform(1000, 3000, n) num_rooms = sqft / 300 + np.random.normal(0, 0.5, n) # Correlated! age = np.random.uniform(5, 50, n) y = 50 * sqft + 10000 * num_rooms - 1000 * age + np.random.normal(0, 20000, n) X = pd.DataFrame({ 'sqft': sqft, 'num_rooms': num_rooms, 'age': age }) # Check correlation matrix print("Correlation Matrix:") print(X.corr().round(3)) # Calculate VIF for each feature print(" Variance Inflation Factors:") for i, col in enumerate(X.columns): vif = variance_inflation_factor(X.values, i) print(f" {col}: {vif:.2f}") # Fit model and observe unstable coefficients lr = LinearRegression() lr.fit(X, y) print(" Coefficients with multicollinearity:") for name, coef in zip(X.columns, lr.coef_): print(f" {name}: {coef:.2f}") # Notice: sqft coefficient is much lower than true 50 # because num_rooms absorbs some of the effect # Demonstration: Remove correlated feature X_no_rooms = X[['sqft', 'age']] lr2 = LinearRegression() lr2.fit(X_no_rooms, y) print(" Coefficients without num_rooms:") for name, coef in zip(X_no_rooms.columns, lr2.coef_): print(f" {name}: {coef:.2f}") # Now sqft coefficient closer to true value76:T477,Regularization (L1/Lasso, L2/Ridge, Elastic Net) fundamentally changes how we should interpret coefficients. Regularization introduces **intentional bias** into coefficient estimates to reduce variance and improve generalization—but this bias affects interpretation. **L2 Regularization (Ridge):** Ridge shrinks all coefficients toward zero but rarely to exactly zero: $$\hat{\beta} = \arg\min \left( \sum(y_i - X_i\beta)^2 + \lambda\sum\beta_j^2 \right)$$ **Interpretation Impact:** Ridge coefficients underestimate true effect sizes. The stronger the regularization (higher $\lambda$), the more coefficients are attenuated toward zero. Comparisons between features remain somewhat valid, but absolute magnitudes are biased. **L1 Regularization (Lasso):** Lasso creates sparse models by driving some coefficients exactly to zero: $$\hat{\beta} = \arg\min \left( \sum(y_i - X_i\beta)^2 + \lambda\sum|\beta_j| \right)$$ **Interpretation Impact:** Non-zero coefficients in Lasso are biased toward zero, often substantially. Zero coefficients indicate 'not selected' rather than 'no effect'. Feature selection depends on $\lambda$ choice.77:T5ea,import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression, Ridge, Lasso # Create data with true known coefficients np.random.seed(42) n, p = 200, 10 # True coefficients: first 5 matter, last 5 are noise true_coefs = np.array([5, 3, 2, 1, 0.5, 0, 0, 0, 0, 0]) X = np.random.randn(n, p) y = X @true_coefs + np.random.randn(n) * 2 # Fit OLS, Ridge, and Lasso ols = LinearRegression().fit(X, y) ridge = Ridge(alpha = 10).fit(X, y) lasso = Lasso(alpha = 0.5).fit(X, y) # Compare coefficients print("True vs Estimated Coefficients:") print(f"{'Feature':<10} {'True':<10} {'OLS':<10} {'Ridge':<10} {'Lasso':<10}") print("-" * 50) for i in range(p): print(f"x{i:<9} {true_coefs[i]:<10.2f} {ols.coef_[i]:<10.2f} " f"{ridge.coef_[i]:<10.2f} {lasso.coef_[i]:<10.2f}") # Key observations: # 1. OLS coefficients are closest to true values # 2. Ridge shrinks all coefficients(even true zeros) # 3. Lasso zeroes out noise features but shrinks others # Stability analysis: How coefficients change with regularization alphas = np.logspace(-2, 2, 50) ridge_coefs = [] lasso_coefs = [] for alpha in alphas: ridge_coefs.append(Ridge(alpha = alpha).fit(X, y).coef_) lasso_coefs.append(Lasso(alpha = alpha, max_iter = 10000).fit(X, y).coef_) ridge_coefs = np.array(ridge_coefs) lasso_coefs = np.array(lasso_coefs) # Features that remain stable across alpha values are more reliable78:T437,Perhaps the most common and dangerous misinterpretation of linear model coefficients is treating them as **causal effects**. A coefficient tells you how the prediction changes with a feature—not what happens if you intervene to change that feature. **The Fundamental Distinction:** - **Observational Association:** When people with higher education have higher incomes, and your model has a positive education coefficient. - **Causal Effect:** What happens to income if you send a random person to college. These are different questions with potentially different answers. The observational association might be driven by confounders (people who go to college might have greater motivation, better opportunities, smarter parents, etc.). **Simpson's Paradox in Linear Models:** Consider the Berkeley admissions example. A model might show negative coefficients for being female when predicting admission. But controlling for department (women applied to more competitive departments), the coefficient becomes positive. The 'effect' entirely depends on what you control for.79:T587,import numpy as np from sklearn.linear_model import LinearRegression # Simulation: Confounding makes coefficient misleading np.random.seed(42) n = 1000 # True causal structure: # Motivation -> Education AND Income(confounder) # Education -> Income(small true effect) motivation = np.random.randn(n) # Unobserved confounder # Both education and income driven by motivation education_years = 12 + 2 * motivation + np.random.randn(n) * 1 income = 30000 + 5000 * motivation + 500 * education_years + np.random.randn(n) * 5000 # True causal effect of education on income: $500 / year # But if we don't control for motivation... # Naive regression(confounded) lr_naive = LinearRegression() lr_naive.fit(education_years.reshape(-1, 1), income) print(f"Naive coefficient: ${lr_naive.coef_[0]:.0f} per year of education") # Output: ~$1, 500 - much higher than true $500! # If we could control for motivation(usually unobserved) X_controlled = np.column_stack([education_years, motivation]) lr_controlled = LinearRegression() lr_controlled.fit(X_controlled, income) print(f"Controlled coefficient: ${lr_controlled.coef_[0]:.0f} per year of education") # Output: ~$500 - close to true effect # Lesson: Without controlling for motivation, # coefficient overstates causal effect 3x # In practice, motivation is unobserved # The naive model PREDICTS well but misleads for intervention7a:T8f9,import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.preprocessing import OneHotEncoder # Example: Salary prediction with department and experience np.random.seed(42) n = 500 departments = np.random.choice(['Engineering', 'Sales', 'HR'], n) experience = np.random.uniform(0, 20, n) # True model: Engineering pays most, experience matters more in Engineering base_salary = 50000 dept_effects = { 'Engineering': 30000, 'Sales': 15000, 'HR': 0 } exp_effects = { 'Engineering': 4000, 'Sales': 2000, 'HR': 1500 } salary = np.array([ base_salary + dept_effects[d] + exp_effects[d] * e + np.random.randn() * 5000 for d, e in zip(departments, experience) ]) # Create DataFrame df = pd.DataFrame({ 'department': departments, 'experience': experience, 'salary': salary }) # One - hot encode departments(drop_first: HR as reference) df_encoded = pd.get_dummies(df, columns = ['department'], drop_first = True) # Model without interaction X_main = df_encoded[['experience', 'department_Engineering', 'department_Sales']] lr_main = LinearRegression().fit(X_main, df_encoded['salary']) print("Model WITHOUT interaction:") print(f" Intercept (HR, 0 years): ${lr_main.intercept_: .0f }") for name, coef in zip(X_main.columns, lr_main.coef_): print(f" {name}: ${coef:.0f}") # Model WITH interaction df_encoded['exp_x_eng'] = df_encoded['experience'] * df_encoded['department_Engineering'] df_encoded['exp_x_sales'] = df_encoded['experience'] * df_encoded['department_Sales'] X_inter = df_encoded[['experience', 'department_Engineering', 'department_Sales', 'exp_x_eng', 'exp_x_sales']] lr_inter = LinearRegression().fit(X_inter, df_encoded['salary']) print(" Model WITH interaction:") print(f" Intercept (HR, 0 years): ${lr_inter.intercept_:.0f}") for name, coef in zip(X_inter.columns, lr_inter.coef_): print(f" {name}: ${coef:.0f}") # Interpretation: # 'experience' coefficient now means experience effect for HR(reference) # 'exp_x_eng' is ADDITIONAL experience effect for Engineering # Total experience effect in Engineering = experience + exp_x_eng7b:T54e,import numpy as np import statsmodels.api as sm # Example: Coefficient uncertainty np.random.seed(42) n_small, n_large = 50, 5000 # True coefficients true_beta = np.array([10, 0.5, 0]) # Strong, weak, zero effect def analyze_sample(n_samples): X = np.random.randn(n_samples, 3) y = X @true_beta + np.random.randn(n_samples) * 5 # Add constant for intercept X_with_const = sm.add_constant(X) model = sm.OLS(y, X_with_const).fit() print(f" Sample size: n = {n_samples}") print(f"{'Feature':<8} {'Coef':>10} {'95% CI':>25} {'p-value':>12}") print("-" * 60) names = ['const', 'x1', 'x2', 'x3'] true_vals = [0, 10, 0.5, 0] for i, name in enumerate(names): coef = model.params[i] ci_low, ci_high = model.conf_int().iloc[i] pval = model.pvalues[i] sig = '***' if pval < 0.001 else '**' if pval < 0.01 else '*' if pval < 0.05 else '' print(f"{name:<8} {coef:>10.3f} [{ci_low:>9.3f}, {ci_high:>9.3f}] {pval:>10.4f} {sig}") # Small sample: uncertainty is large analyze_sample(50) # Large sample: even tiny effects become significant analyze_sample(5000) # Notice: # - x1(true = 10) is significant in both # - x2(true = 0.5) may not be significant at n = 50, highly significant at n = 5000 # - x3(true = 0) stays insignificant regardless of sample size46:["$","$L71",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792298726881,"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":1792298726530,"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":1792298726889,"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:304","prevModule":"$46:props:state:queries:1:state:data:json:path:modules:303","nextModule":"$46:props:state:queries:1:state:data:json:path:modules:305","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.304.createdAt","currentModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.304.updatedAt","prevModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.303.createdAt","prevModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.303.updatedAt","nextModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.305.createdAt","nextModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.305.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.303"],"currentModule":["path.modules.304"],"nextModule":["path.modules.305"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792298726763,"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":"model-specific-interpretability"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getModule\"],{\"input\":{\"contentSlug\":\"model-specific-interpretability\",\"contentType\":\"LEARNING\",\"pathSlug\":\"machine-learning\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"state":{"data":{"json":{"id":"seed_3aa501a5a85f9b61039faeabd0fa7742","slug":"model-specific-interpretability","category":"Machine Learning","topic":"Model-Specific Interpretability","title":"Model-Specific Interpretability","description":"Master the art of interpreting machine learning models using model-specific techniques. Explore linear model coefficients, tree visualizations, attention mechanisms, saliency maps, and concept activation vectors to understand what your models have learned.","difficulty":"ADVANCED","estimatedMinutes":120,"pages":[{"id":"seed_5fc6859ed2fd7f82a50090fbd65fbcb6","title":"Linear Model Coefficients","order":0,"content":[{"id":"opening-hook","title":"The Original Interpretable Model","content":[{"data":{"content":"$72"},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will master the complete framework for interpreting linear model coefficients. You'll understand the mathematical foundations, recognize common interpretation pitfalls, correctly handle different feature scales, navigate multicollinearity, interpret regularized coefficients, distinguish correlation from causation, and apply these principles in real-world scenarios.","variant":"info"},"type":"callout"}]},{"id":"linear-model-foundations","title":"Linear Model Foundations","content":[{"data":{"content":"Before diving into interpretation, we must establish the mathematical framework. A linear model expresses predictions as a weighted sum of input features:\n\n**Linear Regression:**\n$$\\hat{y} = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_p x_p$$\n\n**Logistic Regression:**\n$$P(y=1|x) = \\sigma(\\beta_0 + \\beta_1 x_1 + \\cdots + \\beta_p x_p)$$\n\nwhere $\\sigma(z) = \\frac{1}{1 + e^{-z}}$ is the sigmoid function.\n\nEach coefficient $\\beta_j$ represents the **marginal effect** of feature $x_j$ on the outcome, holding all other features constant. This 'holding constant' assumption is crucial—it's the foundation of coefficient interpretation."},"type":"text"},{"data":{"items":["**Intercept ($\\beta_0$)** — The predicted value when all features equal zero. Often has no meaningful interpretation (e.g., when features are age or salary).","**Feature Coefficients ($\\beta_j$)** — The change in predicted outcome for a one-unit increase in feature $x_j$, assuming all other features remain constant.","**Prediction Function** — The mechanism combining intercept and weighted features to produce outputs.","**Link Function** — In generalized linear models (GLMs), the function connecting the linear predictor to the expected response."],"title":{"icon":"BookOpen","text":"Key Components of Linear Models","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"AlertCircle","title":"The Ceteris Paribus Assumption","content":"The phrase 'holding all other features constant' is easy to say but hard to achieve. In practice, features are often correlated, and changing one feature while holding others constant may be impossible or create unrealistic scenarios. This is a fundamental limitation of coefficient interpretation that we'll address throughout this page.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"coefficient-magnitude","title":"Interpreting Coefficient Magnitude and Sign","content":[{"data":{"content":"The most basic interpretation of a coefficient involves its **sign** and **magnitude**:\n\n**Sign Interpretation:**\n- Positive coefficient ($\\beta_j > 0$): Increasing $x_j$ increases the predicted outcome\n- Negative coefficient ($\\beta_j < 0$): Increasing $x_j$ decreases the predicted outcome\n- Zero coefficient ($\\beta_j = 0$): Feature has no linear effect on outcome\n\n**Magnitude Interpretation (Linear Regression):**\nFor a coefficient $\\beta_j = 2.5$ on a feature measured in thousands of dollars, the interpretation is:\n\n*\"For every additional $1,000 increase in [feature], the predicted [outcome] increases by 2.5 units, holding all other variables constant.\"*"},"type":"text"},{"data":{"title":"Concrete Example: House Price Prediction","content":"Consider a linear regression model for house prices:\n\n$$\\text{Price} = 50,000 + 120 \\times \\text{sqft} + 8,000 \\times \\text{bedrooms} - 1,500 \\times \\text{age}$$\n\n**Interpretation:**\n- **Intercept (50,000)**: Base price when sqft=0, bedrooms=0, age=0 (not meaningful in this context)\n- **sqft coefficient (120)**: Each additional square foot adds $120 to the predicted price\n- **bedrooms coefficient (8,000)**: Each additional bedroom adds $8,000, holding size constant \n- **age coefficient (-1,500)**: Each additional year of age decreases price by $1,500\n\n**Note**: The 'holding constant' caveat matters here. Adding a bedroom typically adds square footage, so the bedroom coefficient captures the value of a bedroom *beyond* the value of its square footage.","description":"Understanding coefficient interpretation in a real estate context"},"type":"example"},{"data":{"content":"**Interpreting Logistic Regression Coefficients:**\n\nLogistic regression coefficients require special handling because the model predicts log-odds, not probabilities directly:\n\n$$\\log\\left(\\frac{P(y=1)}{P(y=0)}\\right) = \\beta_0 + \\beta_1 x_1 + \\cdots$$\n\nThe coefficient $\\beta_j$ represents the **change in log-odds** for a one-unit increase in $x_j$. More intuitively, $e^{\\beta_j}$ gives the **odds ratio**:\n\n- $e^{\\beta_j} > 1$: Increasing $x_j$ increases odds of positive class\n- $e^{\\beta_j} < 1$: Increasing $x_j$ decreases odds of positive class \n- $e^{\\beta_j} = 1$: Feature has no effect on odds\n\nFor small probabilities, odds ratios approximately equal relative risk. For larger probabilities, odds ratios can substantially overstate relative risk."},"type":"text"},{"data":{"fileName":"coefficient_interpretation.py","languages":[{"code":"$73","label":"Python","value":"python"}]},"type":"code"}],"titleType":"number","titleNumber":2},{"id":"standardization-scale","title":"The Scale Problem: Why Standardization Matters","content":[{"data":{"content":"A critical insight that many practitioners miss: **raw coefficients cannot be compared across features with different scales**.\n\nConsider a model predicting loan default with features:\n- Income (range: $20,000 - $500,000)\n- Credit Score (range: 300 - 850)\n- Debt-to-Income Ratio (range: 0 - 2)\n\nIf the coefficients are $\\beta_{income} = -0.00001$, $\\beta_{credit} = -0.01$, and $\\beta_{dti} = 2.5$, which feature is most important? You cannot answer this question from raw coefficients because the scales differ by orders of magnitude.\n\n**The Solution: Standardized Coefficients**\n\nBy standardizing features to have mean 0 and standard deviation 1, coefficients become comparable:\n$$x_j^{\\text{std}} = \\frac{x_j - \\mu_j}{\\sigma_j}$$\n\nStandardized coefficients answer: *\"How many standard deviations does the outcome change for a one standard deviation change in the feature?\"*"},"type":"text"},{"data":{"left":[{"data":{"items":["Depend on original units of measurement","Cannot compare importance across features","Large coefficient may indicate small effect (if feature has tiny scale)","Useful for prediction formula","Interpretation requires knowing units"],"title":{"icon":"XCircle","text":"Raw Coefficients","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Unit-free, scale-independent","Directly comparable across features","Magnitude indicates relative importance","Useful for interpretation","All features on same scale"],"title":{"icon":"CheckCircle","text":"Standardized Coefficients","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"fileName":"standardized_coefficients.py","languages":[{"code":"$74","label":"Python","value":"python"}]},"type":"code"},{"data":{"icon":"Lightbulb","title":"When to Use Which","content":"Use **raw coefficients** when you need exact predictions or care about the effect in original units (e.g., 'each $10,000 income increase reduces default probability by X'). Use **standardized coefficients** when comparing the relative importance of features or communicating which features matter most. Always report both for complete transparency.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"multicollinearity","title":"The Multicollinearity Challenge","content":[{"data":{"content":"One of the most dangerous pitfalls in coefficient interpretation is **multicollinearity**—when predictor variables are correlated with each other. Multicollinearity doesn't prevent you from making good predictions, but it severely undermines coefficient interpretation.\n\n**Why Multicollinearity Breaks Interpretation:**\n\nThe interpretation 'effect of $x_j$ holding other variables constant' becomes problematic when $x_j$ is correlated with other features. If increasing $x_j$ naturally co-occurs with changes in $x_k$, what does 'holding $x_k$ constant' even mean?\n\n**Signs of Multicollinearity:**\n- Coefficients change dramatically when adding/removing correlated features\n- Standard errors are inflated (wide confidence intervals)\n- Coefficients have unexpected signs\n- Model performance is good but individual coefficients seem wrong\n- Variance Inflation Factor (VIF) is high (> 5 or > 10)"},"type":"text"},{"data":{"rows":[["0 - 0.3","1.0 - 1.1","High - Coefficients reliable","Proceed with standard interpretation"],["0.3 - 0.5","1.1 - 1.3","Moderate - Minor inflation","Check confidence intervals"],["0.5 - 0.7","1.3 - 2.0","Low - Notable uncertainty","Consider removing/combining features"],["0.7 - 0.9","2.0 - 5.0","Very Low - High variance","Must address before interpretation"],["0.9 - 1.0","> 5.0","None - Coefficients meaningless","Remove features or use regularization"]],"caption":"Impact of Multicollinearity on Coefficient Interpretation","headers":["Correlation Level","VIF Approximation","Interpretation Reliability","Recommended Action"]},"type":"table"},{"data":{"fileName":"multicollinearity_diagnosis.py","languages":[{"code":"$75","label":"Python","value":"python"}]},"type":"code"},{"data":{"icon":"AlertCircle","title":"Critical Warning","content":"In the presence of high multicollinearity, individual coefficient values become essentially arbitrary. Two equivalent models might have completely different coefficient values for correlated features. Never interpret individual coefficients when VIF > 5 without addressing multicollinearity first.","variant":"error"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"regularization-effects","title":"Regularization Effects on Coefficient Interpretation","content":[{"data":{"content":"$76"},"type":"text"},{"data":{"items":["**Don't interpret magnitudes literally** — Regularized coefficients are intentionally biased; their magnitudes don't represent true effects.","**Relative comparisons are approximate** — Larger coefficients generally indicate more important features, but differences are compressed.","**Zero in Lasso ≠ no effect** — A feature set to zero may have effects that are captured by correlated features or deemed unnecessary given regularization strength.","**Stability across λ values matters** — Robust features remain non-zero across a range of regularization strengths.","**Consider debiasing techniques** — For unbiased estimates, fit regularized model for selection, then unregularized on selected features."],"title":{"icon":"Target","text":"Interpreting Regularized Coefficients","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"fileName":"regularization_interpretation.py","languages":[{"code":"$77","label":"Python","value":"python"}]},"type":"code"},{"data":{"icon":"Info","title":"Post-Selection Inference","content":"For valid statistical inference after Lasso selection, use specialized techniques like selective inference, data splitting, or stability selection. Standard confidence intervals are invalid when the same data is used for both selection and inference.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"causation-correlation","title":"The Causation Problem: Coefficients Are Not Causal Effects","content":[{"data":{"content":"$78"},"type":"text"},{"data":{"rows":[["Question Answered","How does prediction change?","What happens if we intervene?"],["Assumption Required","Model approximates E[Y|X]","No confounding, correct model"],["Validity Context","Often valid for forecasting","Rarely valid without experiments"],["Actionability","For prediction only","For decision-making"],["Example","'Smokers have higher lung cancer risk'","'Quitting smoking reduces risk by X%'"]],"caption":"Coefficient Interpretation: Predictive vs Causal","headers":["Aspect","Predictive Interpretation","Causal Interpretation"]},"type":"table"},{"data":{"icon":"AlertCircle","title":"The Intervention Fallacy","content":"Never say 'increasing X by 1 unit WILL increase Y by β units' unless you have causal identification (randomized experiment, natural experiment, or rigorous causal design). Instead say 'the model prediction increases by β for each unit increase in X' or 'X is associated with β higher Y'.","variant":"error"},"type":"callout"},{"data":{"content":"**When Can Coefficients Be Causal?**\n\nCoefficients approximate causal effects under specific conditions:\n\n1. **Randomized Controlled Trial:** Feature was randomly assigned (rare in observational ML)\n2. **Conditional Ignorability:** You've measured and controlled for all confounders\n3. **Natural Experiment:** Some external factor created as-if random variation\n4. **Instrumental Variables:** Valid instruments create exogenous variation\n5. **Regression Discontinuity:** Sharp threshold creates local randomization\n\nIn typical ML applications, none of these hold. Treat coefficients as associations, not effects."},"type":"text"},{"data":{"fileName":"causal_warning_example.py","languages":[{"code":"$79","label":"Python","value":"python"}]},"type":"code"}],"titleType":"number","titleNumber":6},{"id":"categorical-interactions","title":"Categorical Variables and Interaction Terms","content":[{"data":{"content":"Interpreting coefficients becomes more nuanced with categorical variables and interaction terms. These require understanding **reference categories** and **conditional effects**.\n\n**Categorical Variable Encoding:**\n\nFor a categorical feature with K categories, we typically create K-1 dummy variables. The omitted category becomes the **reference category**, and all coefficients are interpreted relative to it.\n\nExample: Region {North, South, East, West} with North as reference:\n- $β_{South} = 5,000$ means South houses cost $5,000 more than North, on average\n- $β_{East} = -3,000$ means East houses cost $3,000 less than North\n- $β_{West} = 2,000$ means West costs $2,000 more than North\n\n**The choice of reference category affects coefficient signs but not predictions!**"},"type":"text"},{"data":{"icon":"AlertCircle","title":"Dummy Variable Trap","content":"Including all K dummy variables (without dropping one) causes perfect multicollinearity. Always omit one category or use regularization. Be explicit about which category is the reference when reporting results.","variant":"warning"},"type":"callout"},{"data":{"content":"**Interaction Terms:**\n\nInteraction terms allow the effect of one variable to depend on another:\n\n$$y = β_0 + β_1 x_1 + β_2 x_2 + β_3 (x_1 \\times x_2) + ε$$\n\n**Interpretation of Main Effects Changes:**\n\nWith an interaction, $β_1$ is no longer 'the effect of $x_1$'. Instead:\n- $β_1$ = effect of $x_1$ when $x_2 = 0$\n- Actual effect of $x_1$ = $β_1 + β_3 x_2$ (depends on value of $x_2$)\n\nSimilarly, $β_2$ = effect of $x_2$ when $x_1 = 0$."},"type":"text"},{"data":{"fileName":"categorical_interactions.py","languages":[{"code":"$7a","label":"Python","value":"python"}]},"type":"code"}],"titleType":"number","titleNumber":7},{"id":"confidence-significance","title":"Confidence Intervals and Statistical Significance","content":[{"data":{"content":"A coefficient estimate is useless without understanding its uncertainty. **Confidence intervals** and **p-values** quantify this uncertainty, but they're frequently misinterpreted.\n\n**Confidence Interval Interpretation:**\n\nA 95% confidence interval [a, b] for $β_j$ does NOT mean:\n- '95% probability that $β_j$ is between a and b' (Bayesian credible interval meaning)\n\nIt DOES mean:\n- 'If we repeated this experiment many times, 95% of the calculated intervals would contain the true $β_j$'\n\n**Statistical Significance ≠ Practical Importance:**\n\nWith enough data, tiny effects become statistically significant. A coefficient of $0.0001 with p < 0.001 is highly significant but practically meaningless. Conversely, with small samples, large effects might not achieve significance.\n\nAlways report:\n1. Point estimate (coefficient value)\n2. Confidence interval (uncertainty range)\n3. Practical significance (is the effect size meaningful?)"},"type":"text"},{"data":{"rows":[["Large effect, small sample","No","Yes","Inconclusive - need more data"],["Small effect, large sample","Yes","No","Real but ignorable effect"],["Large effect, large sample","Yes","Yes","Confirmed important effect"],["Small effect, small sample","No","No","No evidence of effect"]],"caption":"Statistical vs Practical Significance Matrix","headers":["Scenario","Statistically Significant?","Practically Important?","Interpretation"]},"type":"table"},{"data":{"fileName":"confidence_intervals.py","languages":[{"code":"$7b","label":"Python","value":"python"}]},"type":"code"},{"data":{"icon":"Lightbulb","title":"Reporting Best Practice","content":"Never report just '*p < 0.05*'. Always include: (1) coefficient value, (2) confidence interval, (3) effect size in meaningful units, and (4) domain context for practical significance. A $1/year salary increase might be statistically significant but is practically worthless.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":8},{"id":"summary-best-practices","title":"Summary: Best Practices for Coefficient Interpretation","content":[{"data":{"content":"We've covered the complete framework for interpreting linear model coefficients. Let's consolidate these principles into actionable best practices:"},"type":"text"},{"data":{"type":"ordered","items":["**State the Ceteris Paribus Condition** — Always emphasize 'holding other variables constant' and acknowledge when this is realistic or not.","**Use Standardized Coefficients for Comparison** — Never compare raw coefficient magnitudes across different-scale features.","**Check for Multicollinearity** — Calculate VIF before interpreting. Address high-VIF features before drawing conclusions.","**Understand Regularization Bias** — Regularized coefficients are intentionally shrunk; don't interpret magnitudes literally.","**Never Claim Causation Without Evidence** — Unless you have experimental or quasi-experimental design, coefficients are associations only.","**Report Uncertainty** — Include confidence intervals and discuss both statistical and practical significance.","**Be Explicit About Reference Categories** — For categorical variables, clearly state what category coefficients are relative to.","**Handle Interactions Carefully** — Main effects change meaning in the presence of interactions."],"title":{"icon":"CheckCircle","text":"Core Interpretation Principles","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"Target","title":"Coefficient Interpretation Checklist","content":"Before interpreting any coefficient, verify: (1) Features are appropriately scaled or standardized, (2) VIF is acceptable (< 5), (3) Model assumptions are reasonable, (4) Confidence intervals are available, (5) You're not overclaiming causation, (6) Regularization effects are understood, (7) Reference categories are clear.","variant":"note"},"type":"callout"},{"data":{"content":"**What's Next:**\n\nLinear models offer the clearest form of interpretability, but their simplicity limits their predictive power. In the next page, we'll explore **tree visualization**—how to interpret decision trees and ensemble methods through visual and feature importance analysis. Tree-based methods offer a different paradigm for interpretability that complements linear coefficient analysis."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now have a comprehensive framework for interpreting linear model coefficients. You understand scaling, multicollinearity, regularization effects, causal limitations, and proper statistical reporting. These skills apply whether you're building interpretable models for high-stakes decisions or simply understanding what a linear baseline reveals about your data.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":9}],"isLocked":false},{"id":"seed_cb22f4ff362a904d45361e7eacaff58f","title":"Tree Visualization","order":1,"content":null,"isLocked":true},{"id":"seed_399396af2ffc07e3eb4e424c07bf76f0","title":"Attention Visualization","order":2,"content":null,"isLocked":true},{"id":"seed_f70b2a429f82e5ea1355baddb1d48f78","title":"Saliency Maps","order":3,"content":null,"isLocked":true},{"id":"seed_f724cc23b5ba2d68bdada80937126b14","title":"Concept Activation Vectors","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792298726875,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"model-specific-interpretability"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"model-specific-interpretability\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792298726876,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_3aa501a5a85f9b61039faeabd0fa7742"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_3aa501a5a85f9b61039faeabd0fa7742\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792298726876,"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_f724cc23b5ba2d68bdada80937126b14"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_f724cc23b5ba2d68bdada80937126b14\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792298726876,"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_f724cc23b5ba2d68bdada80937126b14"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_f724cc23b5ba2d68bdada80937126b14\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792298726876,"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_f724cc23b5ba2d68bdada80937126b14"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_f724cc23b5ba2d68bdada80937126b14\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792298726897,"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_f724cc23b5ba2d68bdada80937126b14","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_f724cc23b5ba2d68bdada80937126b14\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@7c"}]},"children":"$L7d"}]