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:T869,import numpy as np import pandas as pd from scipy import stats def analyze_numerical_feature(series: pd.Series, name: str) -> dict: """ Comprehensive analysis of a numerical feature. Returns statistics and recommendations for preprocessing. """ analysis = { "name": name, "dtype": str(series.dtype), "count": len(series), "missing": series.isna().sum(), "missing_pct": series.isna().mean() * 100, "unique_values": series.nunique(), "is_likely_discrete": series.nunique() < 50 and series.dropna().apply(float.is_integer).all(), } # Basic statistics (for non-missing values) clean = series.dropna() if len(clean) > 0: analysis.update({ "min": clean.min(), "max": clean.max(), "mean": clean.mean(), "median": clean.median(), "std": clean.std(), "skewness": stats.skew(clean), "kurtosis": stats.kurtosis(clean), }) # Distribution shape recommendations skew = abs(analysis["skewness"]) if skew > 2: analysis["transform_recommendation"] = "log or sqrt transform (highly skewed)" elif skew > 1: analysis["transform_recommendation"] = "consider log transform (moderately skewed)" else: analysis["transform_recommendation"] = "standard scaling likely sufficient" # Outlier detection using IQR q1, q3 = clean.quantile([0.25, 0.75]) iqr = q3 - q1 outlier_mask = (clean < q1 - 1.5 * iqr) | (clean > q3 + 1.5 * iqr) analysis["outlier_count"] = outlier_mask.sum() analysis["outlier_pct"] = outlier_mask.mean() * 100 return analysis # Example usage np.random.seed(42) income = pd.Series(np.random.lognormal(10, 1, 10000)) # Log-normal income data analysis = analyze_numerical_feature(income, "household_income") print(f"Feature: {analysis['name']}") print(f"Skewness: {analysis['skewness']:.2f}") print(f"Recommendation: {analysis['transform_recommendation']}") print(f"Outliers: {analysis['outlier_pct']:.1f}%")73:T59b,import pandas as pd import numpy as np from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder from category_encoders import TargetEncoder # Sample data df = pd.DataFrame({ "color": ["red", "blue", "green", "red", "blue", "green", "red", "blue"], "size": ["S", "M", "L", "XL", "S", "M", "L", "XL"], "target": [0, 1, 1, 1, 0, 0, 1, 1] }) # 1. One-Hot Encoding for nominal features onehot = OneHotEncoder(sparse_output=False, handle_unknown="ignore") color_encoded = onehot.fit_transform(df[["color"]]) color_columns = onehot.get_feature_names_out(["color"]) print("One-Hot Encoded Colors:") print(pd.DataFrame(color_encoded, columns=color_columns)) # 2. Ordinal Encoding with explicit order size_order = ["S", "M", "L", "XL"] # Smallest to largest ordinal = OrdinalEncoder(categories=[size_order]) df["size_ordinal"] = ordinal.fit_transform(df[["size"]]) print(f" Ordinal Encoded Sizes: {df['size_ordinal'].tolist()}") # 3. Target Encoding (with smoothing to prevent overfitting) target_enc = TargetEncoder(smoothing=1.0) df["color_target_encoded"] = target_enc.fit_transform(df["color"], df["target"]) print(f" Target Encoded Colors: {df[['color', 'color_target_encoded']].drop_duplicates()}") # 4. Frequency Encoding freq_map = df["color"].value_counts(normalize=True).to_dict() df["color_freq"] = df["color"].map(freq_map) print(f" Frequency Encoded Colors: {df[['color', 'color_freq']].drop_duplicates()}")74:Td20,import pandas as pd import numpy as np def infer_feature_type(series: pd.Series, name: str) -> dict: """ Infer semantic feature type from data characteristics. Returns type classification and confidence. """ result = {"name": name, "dtype": str(series.dtype)} clean = series.dropna() n_unique = clean.nunique() n_total = len(clean) unique_ratio = n_unique / n_total if n_total > 0 else 0 # String-like columns if series.dtype == 'object' or str(series.dtype) == 'string': if n_unique == 2: result["inferred_type"] = "binary" result["confidence"] = "high" elif n_unique <= 20: result["inferred_type"] = "categorical_lowcard" result["confidence"] = "high" elif n_unique <= 100: result["inferred_type"] = "categorical_medcard" result["confidence"] = "medium" else: # Could be high-cardinality categorical or text avg_len = clean.astype(str).str.len().mean() if avg_len > 50: result["inferred_type"] = "text" result["confidence"] = "medium" else: result["inferred_type"] = "categorical_highcard" result["confidence"] = "medium" # Numeric columns elif np.issubdtype(series.dtype, np.number): is_integer = clean.apply(lambda x: float(x).is_integer()).all() if n_unique == 2: result["inferred_type"] = "binary" result["confidence"] = "high" elif is_integer and n_unique <= 10: # Low-cardinality integers: could be ordinal, discrete, or categorical result["inferred_type"] = "discrete_or_ordinal" result["confidence"] = "low" result["note"] = "Inspect values to determine if ordinal or categorical" elif is_integer and n_unique <= 50: result["inferred_type"] = "discrete_numerical" result["confidence"] = "medium" elif unique_ratio > 0.9: result["inferred_type"] = "continuous" result["confidence"] = "high" elif unique_ratio > 0.5: result["inferred_type"] = "continuous" result["confidence"] = "medium" else: result["inferred_type"] = "discrete_numerical" result["confidence"] = "medium" # Datetime elif pd.api.types.is_datetime64_any_dtype(series): result["inferred_type"] = "datetime" result["confidence"] = "high" else: result["inferred_type"] = "unknown" result["confidence"] = "low" result["unique_values"] = n_unique result["unique_ratio"] = unique_ratio return result # Apply to a DataFrame def analyze_all_features(df: pd.DataFrame) -> pd.DataFrame: results = [infer_feature_type(df[col], col) for col in df.columns] return pd.DataFrame(results) # Example df = pd.DataFrame({ "user_id": range(1000), "age": np.random.randint(18, 80, 1000), "income": np.random.lognormal(10, 1, 1000), "gender": np.random.choice(["M", "F"], 1000), "education": np.random.choice(["HS", "BS", "MS", "PhD"], 1000), "rating": np.random.randint(1, 6, 1000), "is_premium": np.random.choice([0, 1], 1000), }) print(analyze_all_features(df).to_string())46:["$","$L71",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792296764246,"state":{"data":{"json":{"archivedAt":null,"id":"seed_7f6b8faa6086f2b6271ead87974bea32","slug":"machine-learning-patterns","title":"Machine Learning Patterns","description":"Master Machine Learning from fundamentals to advanced techniques through 19 comprehensive chapters. This path covers 318 carefully curated problems spanning linear algebra, probability, classical ML, deep learning, reinforcement learning, and more.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Classical ML Algorithms","Neural Networks","Deep Learning","Reinforcement Learning","NLP & Computer Vision","MLOps & Deployment"],"estimatedHours":155.6,"problemCount":317,"visualizerCount":15,"quizCount":0,"learningCount":15,"difficulty":"ADVANCED","idealFor":["ML engineers","Data scientists","AI researchers","Software engineers transitioning to ML"],"prerequisites":["Basic Python programming","Understanding of calculus concepts","Familiarity with linear algebra","Basic statistics knowledge"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithm implementation","Neural network design and training","Deep learning architectures","Reinforcement learning techniques","Model evaluation and optimization"],"outcomes":["Build ML models from scratch","Understand and implement core algorithms","Design neural network architectures","Deploy and monitor ML systems","Apply ML to real-world problems"],"overview":"This comprehensive learning path covers 318 problems across 19 chapters, taking you from mathematical foundations to advanced ML techniques.\n\n**Learning Path Structure:**\n• Chapters 1-2: Mathematical Foundations - Linear algebra and probability\n• Chapter 3: Data Preprocessing - Feature engineering and data pipelines\n• Chapter 4: Calculus & Optimization - Gradients and optimization theory\n• Chapter 5-6: Classical ML - Traditional algorithms and evaluation metrics\n• Chapters 7-8: Neural Networks - Fundamentals and training techniques\n• Chapters 9-11: Deep Learning - CNNs, RNNs, and Transformers\n• Chapters 12-14: Advanced Topics - Modern architectures and RL\n• Chapters 15-18: Applications - NLP, CV, LLM evaluation, and MLOps\n\nEach chapter builds on previous concepts for a cohesive learning experience.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.217Z","modules":[{"id":"seed_0fed999e26d2c1b303da9c0446066784","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c8f5199eee51f755e2c074360082ace1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Coordinate Transformation Between Bases","order":2,"contentType":"VISUALIZER","contentSlug":"basis-change-matrix","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_97b73c7659a4b536f5744fbb0b4bfc04","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Coordinate Transformation Between Bases","order":3,"contentType":"PROBLEM","contentSlug":"basis-change-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_903c973d066bfea48b4a823ed2cac448","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Closed-Form Linear Model Fitting","order":4,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b6dab6ca30f6f61ab92fb8bf0e4d491f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Column Compressed Sparse Matrix Format","order":5,"contentType":"PROBLEM","contentSlug":"column-compressed-sparse-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5632f9e5f34a9561ec354d1e40beb96d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Component-wise Vector Addition","order":6,"contentType":"PROBLEM","contentSlug":"component-wise-vector-addition","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6f6e1e827e3cb3af8fe6fd6ccefa0b4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Linear Kernel Computation (Dot Product Similarity)","order":7,"contentType":"PROBLEM","contentSlug":"linear-kernel-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1598b2cf3105e4f03843157652ddce4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Linear Transformation of a Vector","order":8,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3565d8e495f25484809081d148299e41","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Square Matrix Determinant and Trace Evaluator","order":9,"contentType":"PROBLEM","contentSlug":"matrix-determinant-trace-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_cbc8bb101e85ad2a916dfec6da7b6e87","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Matrix Dimension Restructuring","order":10,"contentType":"PROBLEM","contentSlug":"matrix-dimension-restructuring","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1c2b95703f1f152f88770b9594885bb0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":11,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_207e2cec4be1ca1c571596755c670529","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Matrix Row-Column Interchange","order":12,"contentType":"PROBLEM","contentSlug":"matrix-row-column-interchange","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f7091d31b102288e0a69f2cf94268c5b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Scaling a Matrix by a Constant Factor","order":13,"contentType":"PROBLEM","contentSlug":"matrix-scalar-multiplication","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_bb59409c386fe7e029eaf9301363f133","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Polynomial Kernel Similarity Computation","order":14,"contentType":"PROBLEM","contentSlug":"polynomial-kernel-similarity","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d11433d22c48db97f8ffe65350b79e39","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Hyperplane Separation Width for Linear Classifiers","order":15,"contentType":"PROBLEM","contentSlug":"svm-margin-width-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_aa89700510b7518658b7d16193681876","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Three-Dimensional Vector Cross Product","order":16,"contentType":"PROBLEM","contentSlug":"three-dimensional-vector-cross-product","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_faeba8da6151a849971ece0998257c32","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vector Inner Product Computation","order":17,"contentType":"PROBLEM","contentSlug":"vector-inner-product","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c4232feb07cbfcbc6e65b49b2183809d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vector Projection onto a Line","order":18,"contentType":"PROBLEM","contentSlug":"vector-projection-onto-line","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_bad64b9af305e248c2be8416530eb0b5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Construct Diagonal Matrix from Vector","order":19,"contentType":"PROBLEM","contentSlug":"vector-to-diagonal-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a95962ea6c455750ed761d28b4ee7e85","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Column Space Basis Extraction","order":20,"contentType":"PROBLEM","contentSlug":"column-space-basis-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_9e6f03f6c505e038040fe4df423d5c1f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Doubly Stochastic Stream Mixing Forward Pass","order":21,"contentType":"PROBLEM","contentSlug":"doubly-stochastic-stream-mixing","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0bc241e4fdd8c27be93ab6cccf132c5f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Gauss-Seidel Linear System Solver","order":22,"contentType":"PROBLEM","contentSlug":"gauss-seidel-linear-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6c9e2c1e97f2105c250c6860f6a0f054","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Gaussian Kernel Similarity Matrix","order":23,"contentType":"PROBLEM","contentSlug":"gaussian-kernel-similarity-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a6182437e9cfb27f65436eee2ed3ecfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Hyperdimensional Row Encoding Using Bind-Bundle Operations","order":24,"contentType":"PROBLEM","contentSlug":"hyperdimensional-row-encoding","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5c32b1938f6387a424492f58adfb4eab","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Low-Rank Adaptation Forward Computation","order":25,"contentType":"PROBLEM","contentSlug":"low-rank-adaptation-forward","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5856e5818a47891b9a861f34ca8cdb10","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Pairwise Matrix Product Computation","order":26,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_52ca1368702630a521c670495fc4fa95","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Surprise-Driven Neural Memory Update with Momentum","order":27,"contentType":"PROBLEM","contentSlug":"neural-memory-update-surprise-momentum","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a5b9c715441cca71a6f409f381c244fe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Two-Dimensional Orthonormal Basis Generator","order":28,"contentType":"PROBLEM","contentSlug":"orthonormal-basis-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_109d41d107af77032f4728124d1ef433","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Row Reduced Canonical Form","order":29,"contentType":"PROBLEM","contentSlug":"row-reduced-canonical-form","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8a84840694f53c72fe7d4aa1e969e27f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Similarity Matrix Transformation","order":30,"contentType":"PROBLEM","contentSlug":"similarity-matrix-transform","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f0ad4092675213ad0e865668e412989d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Spatial Translation Transformation in 2D","order":31,"contentType":"PROBLEM","contentSlug":"spatial-translation-transform","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_46f7916ce54b82dd2066c09bca95f228","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Spectral Analysis of a Square Matrix","order":32,"contentType":"PROBLEM","contentSlug":"spectral-analysis-square-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e4e297ce0a6812999d072c08747f51a6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_bc8045d3a49673a818445952dfe6b770","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Binary Association Coefficient","order":2,"contentType":"VISUALIZER","contentSlug":"binary-association-coefficient","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_99eeb4f46f0af3e65915450c1b6c3f99","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Binary Association Coefficient","order":3,"contentType":"PROBLEM","contentSlug":"binary-association-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_42915cae18a625eda2d851cad7f13d93","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Dataset Summary Metrics Calculator","order":4,"contentType":"PROBLEM","contentSlug":"dataset-summary-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_481443a6ae37d1c0add7d75d9adad58b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Discrete Frequency Distribution Calculator","order":5,"contentType":"PROBLEM","contentSlug":"discrete-frequency-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f8955cfe865f6f4a0e61f03d43e83d71","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Statistical Moments of a Uniform Die","order":6,"contentType":"PROBLEM","contentSlug":"fair-dice-statistics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e2f5cf634181f1ad5fcd49af54ba7d01","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Information Divergence Between Gaussian Distributions","order":7,"contentType":"PROBLEM","contentSlug":"gaussian-distribution-divergence","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_92c06a1016cca53b87c18f94459bf4b7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Nonlinear Activator","order":8,"contentType":"PROBLEM","contentSlug":"gaussian-nonlinear-activator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6d27d96e55634041fc446917b831c109","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Log Softmax Transformation","order":9,"contentType":"PROBLEM","contentSlug":"log-softmax-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_261f2a0afd9c2ef268045872640a3fdc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Compute Arithmetic Mean Along Matrix Axis","order":10,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e2858d50c8fceacc268a12f2d7a8c5ae","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Computing Event Likelihood Using the Poisson Model","order":11,"contentType":"PROBLEM","contentSlug":"poisson-event-likelihood","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6a4428aaedd87a88cf88792fbfe54c88","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Sample Mean Distribution Simulator","order":12,"contentType":"PROBLEM","contentSlug":"sample-mean-distribution-simulator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_482a188ea8f13767632a86d9372cce41","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Logistic Sigmoid Transformation","order":13,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5a30384cf824beed66ad8701dd0c3bba","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Softmax Probability Distribution","order":14,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c6507663b48270175326bb689e6b81b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Aggregate Probability Computation via Event Partitioning","order":15,"contentType":"PROBLEM","contentSlug":"aggregate-probability-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_55ad8c56b5b3410a66f3295264611dea","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Bernoulli Trial Success Probability","order":16,"contentType":"PROBLEM","contentSlug":"bernoulli-trial-success-probability","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7bad12b008e115f4e4485c7b44e9720f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Chi-Square Distribution Density Evaluator","order":17,"contentType":"PROBLEM","contentSlug":"chi-square-density-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_baa6532bd31f7e84f893b766f9cf15f5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Conditional Likelihood from Joint Event Distribution","order":18,"contentType":"PROBLEM","contentSlug":"conditional-likelihood-from-joint-events","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b274badce3ca232189203f8ec1a640a2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Pearson Correlation Matrix Computation","order":19,"contentType":"PROBLEM","contentSlug":"correlation-matrix-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f1833af17e7ec7d2d543693fa0f515ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Experiment Detection Power Analysis","order":20,"contentType":"PROBLEM","contentSlug":"experiment-detection-power-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_067155b8e858b14423f3a11e7801901d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Finite Sampling Probability Calculator","order":21,"contentType":"PROBLEM","contentSlug":"finite-sampling-probability-calculator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_873a83ec1931f2bdcbd4816612aa9d46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Distribution Divergence Measure","order":22,"contentType":"PROBLEM","contentSlug":"gaussian-distribution-divergence-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fae9aa499ff4a92874361b2616613508","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Probability Density Evaluator","order":23,"contentType":"PROBLEM","contentSlug":"gaussian-probability-density-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3a632d989564f877884bf768d057807a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Independent Samples Mean Comparison","order":24,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_81eb624d8db2a19e99945991e36b5846","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Shared Information from Joint Probability Distribution","order":25,"contentType":"PROBLEM","contentSlug":"joint-distribution-information-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_de4d99ac8f4792f78b7cb6060ff166cd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Computing Covariance from Joint Probability Distributions","order":26,"contentType":"PROBLEM","contentSlug":"joint-pmf-covariance-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_da1b205eeb3f533f39d298a47dc9c8d9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Failure Count Probability Before Success Goal","order":27,"contentType":"PROBLEM","contentSlug":"negative-binomial-failure-probability","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_82fcbfe1a66f542a425ae5e00c65679f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Principal Component Extraction","order":28,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_087a261a23ebffa5552dadb354e7be4e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Shared Date Coincidence Calculator","order":29,"contentType":"PROBLEM","contentSlug":"shared-date-coincidence-calculator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_887a831ac0eb178d9d25ff229253538d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Probability-Weighted Ensemble Classification","order":30,"contentType":"PROBLEM","contentSlug":"soft-voting-ensemble-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7dcdc25128a926e29296ee7c94997b7c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Stochastic State Evolution Simulator","order":31,"contentType":"PROBLEM","contentSlug":"stochastic-state-evolution-simulator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_eec23075bb59f73087781c694f02fa78","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Symmetric Distribution Divergence Measure","order":32,"contentType":"PROBLEM","contentSlug":"symmetric-distribution-divergence-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2df4bfe26b8990979ce54b5ad5a4104c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Bayesian Function Predictor","order":33,"contentType":"PROBLEM","contentSlug":"bayesian-function-predictor","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_dbf5e5a1b2c29e35c52e2bda6e309d7f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0100abc09d0448605acf6efc210cc3b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Activation Function Gradient Computation","order":2,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6445aca82b4c6c784112cd82d058cd08","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Activation Function Gradient Computation","order":3,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ca90fbb7a6603e75f7e251f035e96afb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adamax Gradient Optimizer Update Step","order":4,"contentType":"PROBLEM","contentSlug":"adamax-gradient-optimizer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e694ebf6feab61a29e05d7f015263128","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Gradient Parameter Updater","order":5,"contentType":"PROBLEM","contentSlug":"adaptive-gradient-parameter-updater","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f8ac39dcb399df89ea1573b73b270906","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Binary Classification Loss","order":6,"contentType":"PROBLEM","contentSlug":"binary-classification-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_991f8ffc82bcc2922c28b17b561c32e6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Bounded Gradient Constraint for Training Stabilization","order":7,"contentType":"PROBLEM","contentSlug":"bounded-gradient-constraint","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3c0bd0bb8e486fcb65a908c925cd8a19","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sparse Matrix Compression: Row-Based Storage Transformation","order":8,"contentType":"PROBLEM","contentSlug":"csr-sparse-matrix-conversion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_eee0ed04779c005c65c278cd5834e426","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Epoch-Based Learning Rate Decay Scheduler","order":9,"contentType":"PROBLEM","contentSlug":"epoch-based-learning-rate-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f7ba97adcb9ac42115512f9cb8c9eb24","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Exponential Decay Learning Rate Scheduler","order":10,"contentType":"PROBLEM","contentSlug":"exponential-decay-learning-rate","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_765811a293443f5276de0c162c60f4fa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":11,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_be6b57dd476ff3803fbc4179e89098c4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Vector Analysis for Optimization","order":12,"contentType":"PROBLEM","contentSlug":"gradient-vector-analysis","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_cf1085ea55d62ff1c7b8e65b2ed17110","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Descent with L2 Regularization Penalty","order":13,"contentType":"PROBLEM","contentSlug":"l2-regularization-weight-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2f78e0cb7b8c967b23a4c6340b985469","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Margin Penalty Loss Computation for Binary Classifiers","order":14,"contentType":"PROBLEM","contentSlug":"margin-penalty-loss-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e99b7b03bc660cae78cc3c7448fef5b7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Momentum-Based Parameter Updater","order":15,"contentType":"PROBLEM","contentSlug":"momentum-based-parameter-updater","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a4c6878e675339d6123fc6cbe0dda305","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Nesterov Accelerated Momentum Optimizer","order":16,"contentType":"PROBLEM","contentSlug":"nesterov-momentum-optimizer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0540bc4ca1a7b3f0c5b0dc7b7e143cad","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Piecewise Linear Sigmoid Approximation","order":17,"contentType":"PROBLEM","contentSlug":"piecewise-linear-sigmoid-approximation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4c5cc0b61a6b01e7b4f64d4bc1cc6274","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Series Function Estimation","order":18,"contentType":"PROBLEM","contentSlug":"polynomial-series-function-estimation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_14ae101cb19b475926980112999bca78","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Term Rate of Change","order":19,"contentType":"PROBLEM","contentSlug":"polynomial-term-derivative","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fe87c5037abd4d567d45e6d275edef09","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sequential Function Chain Evaluator","order":20,"contentType":"PROBLEM","contentSlug":"sequential-function-chain-evaluator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f2753046c950e86a85fa65b38c0a50f3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training Convergence Halt Detector","order":21,"contentType":"PROBLEM","contentSlug":"training-convergence-halt-detector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_83eec4b77169e413ac846a2084df5f81","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training Halt Monitor: Validation Stagnation Detector","order":22,"contentType":"PROBLEM","contentSlug":"training-halt-monitor","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e5adc58490f27c31475a789d6d3b2caf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Delta Gradient Optimizer","order":23,"contentType":"PROBLEM","contentSlug":"adaptive-delta-gradient-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_54f835afd6abc355051c6b275ff98698","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Gradient Scaling Optimizer","order":24,"contentType":"PROBLEM","contentSlug":"adaptive-gradient-scaling-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5fd25ee096bf2f1be0b29cca6f4ab810","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Moment Estimation Optimizer Step","order":25,"contentType":"PROBLEM","contentSlug":"adaptive-moment-estimation-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_afe9920decead522af47917f2870be60","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Moment Estimation Optimizer","order":26,"contentType":"PROBLEM","contentSlug":"adaptive-moment-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a8149de2ae9f67239f84c42e7a5ea215","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Segmented 8-Bit Floating Point Tensor Compression","order":27,"contentType":"PROBLEM","contentSlug":"blockwise-fp8-quantization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_031e7d71c2c9cbab94be812cba78de07","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Composite Function Differentiation via Chain Rule","order":28,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1d70ad9216f470dffc457cc6d43d0f8f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Constrained Quadratic Optimizer with Dual Variables","order":29,"contentType":"PROBLEM","contentSlug":"constrained-quadratic-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c878243e2f770ed9c6307374b7904496","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Cosine Decay Learning Rate Scheduler","order":30,"contentType":"PROBLEM","contentSlug":"cosine-annealing-learning-rate-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_38944d0a6de3f4668782e5292ad025aa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Curvature-Based Optimization Step","order":31,"contentType":"PROBLEM","contentSlug":"curvature-based-optimization-step","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c940806ae1635c5228535651eb66f1a0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Decoupled Weight Decay Parameter Update","order":32,"contentType":"PROBLEM","contentSlug":"decoupled-weight-decay-parameter-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_851e52c008dd224a995132fde05ec2db","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Determinant-Based Linear System Solver","order":33,"contentType":"PROBLEM","contentSlug":"determinant-based-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4e5979faf45510a1b37ef933b81d44bd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Exhaustive Hyperparameter Search","order":34,"contentType":"PROBLEM","contentSlug":"exhaustive-hyperparameter-search","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_97b4c83f1970fd3799453c99222712e7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Direct Linear System Solver Using Row Reduction","order":35,"contentType":"PROBLEM","contentSlug":"gaussian-elimination-linear-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a1aaadaa75eef9954e0be670780bd9a9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Global Norm Gradient Rescaling","order":36,"contentType":"PROBLEM","contentSlug":"global-norm-gradient-rescaling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3b09a3b663e1b262860de1ece518d31b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Glorot/Xavier Weight Initialization","order":37,"contentType":"PROBLEM","contentSlug":"glorot-weight-initialization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f0284703f3c55a1b107ed0b3b4acacfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Field Matrix Computation","order":38,"contentType":"PROBLEM","contentSlug":"gradient-field-matrix-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5593f671425ea59616856d47e1664cd0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Vector Computation for Scalar Fields","order":39,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c0c5b22fe6852b5e5223d47a4c128743","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Verification via Finite Difference Approximation","order":40,"contentType":"PROBLEM","contentSlug":"gradient-verification-finite-difference","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_aae5bf36a7ab80d47f70e2a390e82f46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hessian-Based Critical Point Classification","order":41,"contentType":"PROBLEM","contentSlug":"hessian-critical-point-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_810e4f3b9c43539eb94eaf8603d474c6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hybrid Precision Computation for Deep Learning","order":42,"contentType":"PROBLEM","contentSlug":"hybrid-precision-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2b19216f6da8e6ed2c50a9548b43e60b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hybrid Regularized Linear Optimizer","order":43,"contentType":"PROBLEM","contentSlug":"hybrid-regularized-linear-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7524eebcf106ebef5b2ac0efa2516f0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Iterative Linear System Solver","order":44,"contentType":"PROBLEM","contentSlug":"iterative-linear-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_51b3da489a4f97a6463ef65c3fcbc251","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Momentum-Based Orthogonal Optimizer with Iterative Matrix Conditioning","order":45,"contentType":"PROBLEM","contentSlug":"muon-optimizer-newton-schulz-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ad754ffaa9e27b2907c6ae66d797e950","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Muon Optimizer Update with Newton-Schulz Preconditioning","order":46,"contentType":"PROBLEM","contentSlug":"muon-optimizer-step","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f9fd8d72df8d22b797c04cccd4b03353","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Parameter Optimization Using Gradient-Based Algorithms","order":47,"contentType":"PROBLEM","contentSlug":"optimization-step-algorithms","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0654867f33edc12c165d8e0d136f97ef","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Global Minimum Finder","order":48,"contentType":"PROBLEM","contentSlug":"polynomial-global-minimum-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1afd7f6594073b2cb1635a2ffebe704a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Derivative of Polynomial Ratio Using Division Rule","order":49,"contentType":"PROBLEM","contentSlug":"polynomial-quotient-derivative","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_42fc70bfbeb049dbf9f607647ce1aecd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Quantized Low-Rank Adaptation Forward Pass","order":50,"contentType":"PROBLEM","contentSlug":"quantized-lowrank-adaptation-forward","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_22217ca3bcec23d1fe47134e588781b6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Robust Hybrid Regression Loss","order":51,"contentType":"PROBLEM","contentSlug":"robust-hybrid-regression-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2a8692ae0dd2ee91b0fa92a053608bf2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Second-Order Curvature Matrix Computation","order":52,"contentType":"PROBLEM","contentSlug":"second-order-curvature-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8d89700158fb5363c6fb0422ac14c330","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Masked Gradient Descent for Localized Knowledge Editing","order":53,"contentType":"PROBLEM","contentSlug":"selective-gradient-masking-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_21bfc8b82c137747c6d80787d42f17d2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training a Single Neuron with Gradient Descent","order":54,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b009e73490398c8df40dcf690a773d79","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient of Softmax Cross-Entropy Loss","order":55,"contentType":"PROBLEM","contentSlug":"softmax-cross-entropy-gradient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_18ce8af11e0618babea3c8b86911a027","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Jacobian Matrix of the Softmax Transformation","order":56,"contentType":"PROBLEM","contentSlug":"softmax-jacobian-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c2355f8cc7fa20fbf9f3c572371e2a2b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sparse Regression Optimizer with L1 Penalty","order":57,"contentType":"PROBLEM","contentSlug":"sparse-regression-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5d03e076a949d371eec2b7722538e7e3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Dynamic Temperature Scheduling for Stochastic Neural Networks","order":58,"contentType":"PROBLEM","contentSlug":"temperature-decay-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5023651c6676e627a90ba7d195b58d44","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Warmup Cosine Annealing Learning Rate Scheduler","order":59,"contentType":"PROBLEM","contentSlug":"warmup-cosine-annealing-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0d437d616850321420fa0c57d5b97753","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Binary Classifier Gradient Optimizer","order":60,"contentType":"PROBLEM","contentSlug":"binary-classifier-gradient-optimizer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f4333d7d25b22a60330042f946838d44","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Iterative Solution of Symmetric Positive-Definite Linear Systems Using Conjugate Gradients","order":61,"contentType":"PROBLEM","contentSlug":"cg-linear-system-solver","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e1203a84133f433d3f68b92ae2332e2c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Computing the Determinant of a 4×4 Matrix via Cofactor Expansion","order":62,"contentType":"PROBLEM","contentSlug":"determinant-4x4-laplace-expansion","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b3943d9a73df36daca86433b2c3f71a4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Two-by-Two Matrix Decomposition via Jacobi Rotation","order":63,"contentType":"PROBLEM","contentSlug":"jacobi-singular-value-decomposition","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_509e0bba90db9d87b3b5562d5bbe2d8c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Kernel-Based Support Vector Classifier Training","order":64,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0280ec8acc24668cb80fe79098df5c53","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Orthogonal-Triangular Matrix Factorization","order":65,"contentType":"PROBLEM","contentSlug":"orthogonal-triangular-factorization","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_525aa14ed693200b9b27d7361691dbc3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Decomposing a 2×2 Matrix via Singular Value Factorization","order":66,"contentType":"PROBLEM","contentSlug":"singular-value-decomposition-2x2","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_955dba231e8d596d43cc5331020e0978","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Softmax Classifier Optimization via Gradient Descent","order":67,"contentType":"PROBLEM","contentSlug":"softmax-classifier-optimization","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_66cf640e44c4fe692c4b5647b652e995","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a95179e6f90ec1d2735e5938f915643a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Binary Encoding","order":2,"contentType":"VISUALIZER","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8d495474fc606fdb2fd0a0fa3b1a2d60","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Binary Encoding","order":3,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_10cea7ba6ccc217ea27fd05609a1f7b9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Distribution Entropy","order":4,"contentType":"PROBLEM","contentSlug":"categorical-distribution-entropy","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_681ef49b03a8c2d3406181cce3bcd37c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Dataset Batch Generator for Training Pipelines","order":5,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e31d188fd627a103fd3798bca5161a92","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Feature Covariance Computation","order":6,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ab66e556b62230c65f315fe38843d27b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":7,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6e62fe0b6131035bbd392c1383a033f5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Interval Normalization Transform","order":8,"contentType":"PROBLEM","contentSlug":"interval-normalization-transform","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4b797764424d7da343050834482e9a59","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Polynomial Basis Expansion for Feature Engineering","order":9,"contentType":"PROBLEM","contentSlug":"polynomial-basis-expansion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_351dac0d10dbcb5dce1f2d21996e5d7d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Range-Bounded Feature Scaling","order":10,"contentType":"PROBLEM","contentSlug":"range-bounded-feature-scaling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0d62d7870facaa1c45b65ed1489b49ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Synchronized Data Permutation","order":11,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2d73f65475af3cd309db22bcd92ea617","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Ordered Polynomial Feature Generator","order":12,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7df2eaa815f1ec39988e72a494ba5ad0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Partition Dataset by Feature Threshold","order":13,"contentType":"PROBLEM","contentSlug":"partition-data-by-threshold","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_53c658f4b182b34f946cb96c13a3b347","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Proportional Class Partitioning for Dataset Splitting","order":14,"contentType":"PROBLEM","contentSlug":"proportional-class-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d9e14b161ee8138ac0860422461259da","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Random Dataset Subset Generation for Ensemble Learning","order":15,"contentType":"PROBLEM","contentSlug":"random-dataset-subsets","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_72021d593fe624b8eebf1d57d65abc0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a0977e62147804e59e048f5d910941c9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Categorical Cross-Entropy Loss Computation","order":2,"contentType":"VISUALIZER","contentSlug":"categorical-cross-entropy-loss","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_dc1be4093a2ecfc83d90373fbcab0e86","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Categorical Cross-Entropy Loss Computation","order":3,"contentType":"PROBLEM","contentSlug":"categorical-cross-entropy-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d5975b6bbe0ff570428e162b16888d45","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Majority Vote Ensemble Classifier","order":4,"contentType":"PROBLEM","contentSlug":"majority-vote-ensemble-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4f7d8b7b0d3629f717d222b068b0ce62","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"L2-Regularized Linear Regression Loss Computation","order":5,"contentType":"PROBLEM","contentSlug":"regularized-linear-regression-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_207742d11d88842644f10865e713a36a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Sigmoid-Based Binary Classifier","order":6,"contentType":"PROBLEM","contentSlug":"sigmoid-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_535b94f4cf2b06f9f8eeaf23ba6237fe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":7,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_59c9218455f2aeea9d3f3979501bb1ed","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_22b2a135e9cbaa4897364db60fba644e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Decision Tree Complexity Threshold Analysis","order":9,"contentType":"PROBLEM","contentSlug":"decision-tree-complexity-threshold-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_46e66455ec384966b4298b5ded636fc4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Density-Based Spatial Clustering","order":10,"contentType":"PROBLEM","contentSlug":"density-based-spatial-clustering","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_16e5b8e81ff39d4a96dbd645a3bb9765","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Energy Demand Forecasting with Seasonal Detrending","order":11,"contentType":"PROBLEM","contentSlug":"energy-demand-forecasting-with-detrending","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_dd59ca1b70a461bf9a7a042dc22efb5e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Focused Loss Computation for Class Imbalance","order":12,"contentType":"PROBLEM","contentSlug":"focused-loss-imbalanced-classification","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_543709c06a2f6bb1b6ed3d0659086877","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Information Gain Split Finder","order":13,"contentType":"PROBLEM","contentSlug":"information-gain-split-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_766922035a5ee2a9d88cbed8892aaf58","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Optimal Gini Split Finder for Binary Classification Trees","order":14,"contentType":"PROBLEM","contentSlug":"optimal-gini-split-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e07ea8bb8ce6508523c05dc51d9c2965","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Regression Tree Predictor","order":15,"contentType":"PROBLEM","contentSlug":"regression-tree-predictor","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3e9df52e7744d108d736d0aa82fed79e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Soft Target Classification Loss","order":16,"contentType":"PROBLEM","contentSlug":"soft-target-classification-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a03ceb34b458f144843233ffba16ccaa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"AdaBoost Ensemble Classifier Training","order":17,"contentType":"PROBLEM","contentSlug":"adaboost-ensemble-training","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_165d8c0eb6ca80ef55ce47b9e6578c26","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Recursive Classification Tree Builder","order":18,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fea4e09050bb21ca355f2100df5d88e9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c44fafe12687fdd81ae40c4c76200295","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Adaptive Hyperbolic Tangent Transformation","order":2,"contentType":"VISUALIZER","contentSlug":"adaptive-hyperbolic-tangent-transformation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5a51e7cab867b9be59e87ef9999df60e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Adaptive Hyperbolic Tangent Transformation","order":3,"contentType":"PROBLEM","contentSlug":"adaptive-hyperbolic-tangent-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_83e0d18d3f6f9d32f9bc37574664b49d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Bounded Linear Activation Function","order":4,"contentType":"PROBLEM","contentSlug":"bounded-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b07e2fae5804803dbc9daedc5efc8bca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Exponential Linear Activation Function","order":5,"contentType":"PROBLEM","contentSlug":"exponential-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_115b6a51aa3c3f6b830573df7709abfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Hyperbolic Tangent Activation Function","order":6,"contentType":"PROBLEM","contentSlug":"hyperbolic-tangent-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_92649f6b13a26e8a0d399d3baee1a6c1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Kaiming Weight Initialization Strategy","order":7,"contentType":"PROBLEM","contentSlug":"kaiming-weight-setup","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e837e7c97ce855f2430fb58705cddcc1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Gating Smooth Activation: The Mish Function","order":8,"contentType":"PROBLEM","contentSlug":"mish-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fe84f813221cfd30f6eb60f0965b5e4c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Parametric Rectified Linear Activation","order":9,"contentType":"PROBLEM","contentSlug":"parametric-rectified-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f6c433fc8c08d839cd55fb7702d78eb7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Rectified Linear Activation Function","order":10,"contentType":"PROBLEM","contentSlug":"rectified-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ad9707d65b31922c79380e64bb70ba24","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Gated Neural Activator","order":11,"contentType":"PROBLEM","contentSlug":"self-gated-neural-activator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e92de11877baf74953e005e522c32d0d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Normalizing Exponential Activation","order":12,"contentType":"PROBLEM","contentSlug":"self-normalizing-exponential-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_38fe9ea38f8141e3c53cc30d0d479531","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Skip Connection Residual Unit","order":13,"contentType":"PROBLEM","contentSlug":"skip-connection-residual-unit","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_6e926d6c6a84d594dd89d2ea8c8d17ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Smooth Rectifier Activation Function","order":14,"contentType":"PROBLEM","contentSlug":"smooth-rectifier-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fe2c44936ce7f758794a0878079c99f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Bounded Smooth Activation: The Softsign Function","order":15,"contentType":"PROBLEM","contentSlug":"softsign-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_202207e82d633377a5a82ee3e4046e94","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Sparse Expert Efficiency Ratio","order":16,"contentType":"PROBLEM","contentSlug":"sparse-expert-efficiency-ratio","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0fd30ecaa9b1ff0741d18922b6016edc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Activation Substitution for Neural Circuit Analysis","order":17,"contentType":"PROBLEM","contentSlug":"activation-substitution-circuit-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f512bcfb0fabdfd58069dd100cd88b20","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Gradient Computation Engine (Basic Autograd)","order":18,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b5dd2777774ef15a1a23a5a1d7a27e11","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Teacher-Student Soft Target Transfer Loss","order":19,"contentType":"PROBLEM","contentSlug":"knowledge-distillation-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_60658fa2562fb8ad1ad5c143a9a066c0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Neural Network Trainable Parameter Counter","order":20,"contentType":"PROBLEM","contentSlug":"neural-network-parameter-counter","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c72ba3e07536ddde11f277da2ba50feb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"RLHF Pre-training Regularization Loss for Knowledge Retention","order":21,"contentType":"PROBLEM","contentSlug":"rlhf-pretraining-regularization-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_51d427355434ba6c870e513998e26344","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Stochastic Neuron Deactivation for Network Regularization","order":22,"contentType":"PROBLEM","contentSlug":"stochastic-neuron-deactivation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_63dca2a5669d7bd6ec9f28d572d1c4af","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Stochastic Top-K Expert Routing for Mixture-of-Experts Networks","order":23,"contentType":"PROBLEM","contentSlug":"stochastic-topk-expert-routing","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2a88be68b95638b537b405fa7f37c40b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Building a Fully Connected Neural Network Layer","order":24,"contentType":"PROBLEM","contentSlug":"fully-connected-layer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_63487fa407d9151beddb3858b22d8ce3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c2fa590a27b36d87f3228ad6ab06f39c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-wise Batch Normalization for 4D Feature Maps","order":2,"contentType":"VISUALIZER","contentSlug":"batch-normalization-feature-maps","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_10079aaf1b02beab73f273c232c9b590","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-wise Batch Normalization for 4D Feature Maps","order":3,"contentType":"PROBLEM","contentSlug":"batch-normalization-feature-maps","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a09a68891c8d023732e2fe0212e138f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Grayscale Image Luminance Analyzer","order":4,"contentType":"PROBLEM","contentSlug":"grayscale-image-luminance-analyzer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f47b94dc143e3e5843d71998f24cced4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Grayscale Image Intensity Range Analyzer","order":5,"contentType":"PROBLEM","contentSlug":"grayscale-intensity-range-analyzer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e73e38a83199324361ca7707cf72f343","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Image Border Padding with Zeros","order":6,"contentType":"PROBLEM","contentSlug":"image-border-padding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e801096905ec74483f2a795555266652","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Mirror Image Transformation","order":7,"contentType":"PROBLEM","contentSlug":"mirror-image-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7323baaa7ee35d2ff17c20adf0f02c22","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Perceptual Grayscale Conversion Using Luminosity Weights","order":8,"contentType":"PROBLEM","contentSlug":"rgb-grayscale-luminosity-conversion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_cde9f59eff08a3b365eabecc22f81ea1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Spatial Channel Aggregation","order":9,"contentType":"PROBLEM","contentSlug":"spatial-channel-aggregation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4dd0ac1e37f1042a8f65373c3c628c4d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Spatial Mean Downsampling","order":10,"contentType":"PROBLEM","contentSlug":"spatial-mean-downsampling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3d43ab3d8483eb0902cd8c3f689ff5f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Smooth Image Rescaling with Bilinear Interpolation","order":11,"contentType":"PROBLEM","contentSlug":"bilinear-image-scaling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_faf894d2be1d3dc9f86b202091f454b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel Group Normalization","order":12,"contentType":"PROBLEM","contentSlug":"channel-group-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_414c59892f922a40a42059a758530f95","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-Wise Spatial Normalization","order":13,"contentType":"PROBLEM","contentSlug":"channel-wise-spatial-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_285d7bb02cc7da571b76166e9ecbf2d9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"2D Convolution Operation","order":14,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_70848c79ff84428173ff16ef6bd148fb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Cross-Channel Lateral Inhibition Normalization","order":15,"contentType":"PROBLEM","contentSlug":"cross-channel-lateral-inhibition-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0d1414e65eebc633cf420299c30ac116","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Gradient-Based Edge Detection","order":16,"contentType":"PROBLEM","contentSlug":"gradient-based-edge-detection","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d2b882a92457ba62796ded1b60c08818","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Motion Vector Endpoint Error Metric","order":17,"contentType":"PROBLEM","contentSlug":"motion-vector-endpoint-error","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1179f33606b1868c6090bda1889a57e3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Derivative of Polynomial Product Using Product Rule","order":18,"contentType":"PROBLEM","contentSlug":"polynomial-product-derivative","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8e097c0deac496b6dd53b4d86b1c3cca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Strided Maximum Pooling with Overlap","order":19,"contentType":"PROBLEM","contentSlug":"strided-maximum-pooling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fb519a3a84940622c9cb976b78a92b70","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Bounding Box Overlap Filtering","order":20,"contentType":"PROBLEM","contentSlug":"bounding-box-overlap-filtering","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8bd981e9938c70b1149fe77a73d46865","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Convolutional Network Backpropagation Trainer","order":21,"contentType":"PROBLEM","contentSlug":"convolutional-network-backpropagation-trainer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_4a79d013931cdf445e6725dd5bbb6e26","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Densely Connected Convolutional Block","order":22,"contentType":"PROBLEM","contentSlug":"densely-connected-convolutional-block","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2d8ab26f28f5425c26238859baa4d3cf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"PCA-Based Color Perturbation for Image Augmentation","order":23,"contentType":"PROBLEM","contentSlug":"pca-color-perturbation","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f85c4e762d96fa80631281a637fcde0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Volumetric Convolution Forward Pass","order":24,"contentType":"PROBLEM","contentSlug":"volumetric-convolution-forward","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_be57b7bc82c29511189fafe0174579f6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ba53d154d498d2821d376ba1f49f7fc1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":2,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0216f55b04efd23ea90cc8c1b651a511","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":3,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_958957016e1944702298a2f2253cd33f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Sequential Memory Unit: Building an RNN Cell","order":4,"contentType":"PROBLEM","contentSlug":"recurrent-neural-network-cell","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_e324f0a9415b135764a389093be1f3ff","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Recurrent Network with Temporal Gradient Learning","order":5,"contentType":"PROBLEM","contentSlug":"recurrent-network-temporal-gradient-learning","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_93274150806e24b52074cafc93134800","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a658fbb6a1ef02163602cf7e1f493a33","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Linear Unit with Swish Activation","order":2,"contentType":"VISUALIZER","contentSlug":"swiglu-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_eb66bacb9a21462b23859c660efefffe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Linear Unit with Swish Activation","order":3,"contentType":"PROBLEM","contentSlug":"swiglu-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_806c97e66d42b9c114f25d163c3be64f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Head Pruning for Model Compression","order":4,"contentType":"PROBLEM","contentSlug":"attention-head-pruning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_702929440ba3c51f46e5f72ed22c2628","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Score Clipping Stabilizer","order":5,"contentType":"PROBLEM","contentSlug":"attention-score-clipping-stabilizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d4e2a5d014ea89b279cdc6f9d8090d22","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Causal Attention Mechanism","order":6,"contentType":"PROBLEM","contentSlug":"causal-attention-mechanism","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_28d74420daeefb237ec02cb321ee772b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Two-Layer Feed-Forward Network with Skip Connection and Dropout","order":7,"contentType":"PROBLEM","contentSlug":"feedforward-residual-block","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0fcc67dbdcc5503491a200af38905f0f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Attention Mechanism","order":8,"contentType":"PROBLEM","contentSlug":"gated-attention-mechanism","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b977df7a72ce1419ddbdcbfb2f20b80e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Localized Sliding Attention Mechanism","order":9,"contentType":"PROBLEM","contentSlug":"localized-sliding-attention","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d8f13d1e29c05e14273763444258c9f7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Scaled Dot-Product Attention","order":10,"contentType":"PROBLEM","contentSlug":"scaled-dot-product-attention","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_670ad63c5194722054a8f71ebc1785bc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Self-Attention Weighted Aggregation","order":11,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f63eacdfe6c6ae4c24c1f7f557cf1ac4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sequence-Wise Feature Normalization with Affine Transform","order":12,"contentType":"PROBLEM","contentSlug":"sequence-layer-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_85bedfdefabb589697d29726acd50556","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sparse Mixture-of-Experts: Dynamic Expert Selection and Aggregation","order":13,"contentType":"PROBLEM","contentSlug":"sparse-moe-expert-selection","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c1110669e526e41f63d47c0d51191af8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Autoregressive Text Synthesis Engine","order":14,"contentType":"PROBLEM","contentSlug":"autoregressive-text-synthesis-engine","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0fcae7e31875bd39e87bb69fb1a68228","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Memory-Efficient Tiled Attention Forward Pass","order":15,"contentType":"PROBLEM","contentSlug":"memory-efficient-tiled-attention","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_83516eb68594d2d1dadc657c1458eacc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Parallel Attention Heads Mechanism","order":16,"contentType":"PROBLEM","contentSlug":"parallel-attention-heads","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_cdd8b28151acf4819ab9f5bf12270d37","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sparse Mixture of Experts Forward Pass","order":17,"contentType":"PROBLEM","contentSlug":"sparse-mixture-of-experts-layer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_137913d5151d76195708e9a5217cd078","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Transformer Position Embedding Generator","order":18,"contentType":"PROBLEM","contentSlug":"transformer-position-embedding-generator","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_9592d0f9c2298c263eb144f1fa1cbd7a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a1e329dc598f74898f3455ba5ff8fd6c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Bidirectional Diffusion Transformation","order":2,"contentType":"VISUALIZER","contentSlug":"bidirectional-diffusion-transformation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_bdef5ac0508c6e61dc113a2295d4a85c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Bidirectional Diffusion Transformation","order":3,"contentType":"PROBLEM","contentSlug":"bidirectional-diffusion-transformation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_31900c007005e4b78ddb6fce7af4d2e9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Diffusion Model Denoising Reconstruction Error","order":4,"contentType":"PROBLEM","contentSlug":"diffusion-denoising-reconstruction-error","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_04f600711ee7632b27ed8363d7293b31","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Diffusion Noise Propagation","order":5,"contentType":"PROBLEM","contentSlug":"diffusion-noise-propagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ec928614cfedc037de80e554355cf03c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Optimal Game Move Strategy","order":6,"contentType":"PROBLEM","contentSlug":"optimal-game-move-strategy","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_a96c66111fc8c3c56e1e6a3338ae833c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Adversarial Network for Gaussian Distribution Synthesis","order":7,"contentType":"PROBLEM","contentSlug":"adversarial-network-gaussian-synthesis","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7d09485c35a830c32d1c11d2c6a549bb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c2adbae43cbe24000b4872a2dc57b11b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Cumulative Reward with Temporal Decay","order":2,"contentType":"VISUALIZER","contentSlug":"cumulative-reward-with-decay","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1008aa325b59e159096b646189e3f6ba","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Cumulative Reward with Temporal Decay","order":3,"contentType":"PROBLEM","contentSlug":"cumulative-reward-with-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_66afc79a41950f75d1807f303c0eba7e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Exploration Bonus Action Selector","order":4,"contentType":"PROBLEM","contentSlug":"exploration-bonus-action-selector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5074de0ef184e1f86658cec1c7008dc0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Intra-Group Advantage Normalization for Policy Training","order":5,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_06765e3986ec0390af33ae39c3238190","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":6,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2d0a577a61d4c4b034524393796e03ee","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Sample Accuracy and Consensus Voting Metrics","order":7,"contentType":"PROBLEM","contentSlug":"sample-accuracy-consensus-voting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_38caafedb9bbf78603c6ae2f7ec7e728","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Streaming Average Update for Real-Time Estimation","order":8,"contentType":"PROBLEM","contentSlug":"streaming-average-update","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2aac07bb304082ef1d9c9ffa43db31b6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Bellman Prediction Error for State Value Updates","order":9,"contentType":"PROBLEM","contentSlug":"temporal-difference-error","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_1d271ab1f2f46a8eb25ae5619a672955","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Trajectory Cumulative Discounted Reward","order":10,"contentType":"PROBLEM","contentSlug":"trajectory-cumulative-discounted-reward","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_37f508803c435594daeac64f33d7edf2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Adversarial Critic Reward Computation","order":11,"contentType":"PROBLEM","contentSlug":"adversarial-critic-reward-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f8c570344424f21bb461a0e2a65c7d13","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Token-Budget Aware Reinforcement Learning Loss","order":12,"contentType":"PROBLEM","contentSlug":"budget-aware-rl-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_0452073c4dce23c7e260087919297866","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Exploration-Exploitation Action Chooser","order":13,"contentType":"PROBLEM","contentSlug":"exploration-exploitation-action-chooser","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3cb269b402c08d7c5e09c3c6acdd13a4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Grid Navigation Value Estimation","order":14,"contentType":"PROBLEM","contentSlug":"grid-navigation-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_c6fde09daa7d32998d81894e7fbae534","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Initial Encounter Value Estimation from Episodic Experience","order":15,"contentType":"PROBLEM","contentSlug":"initial-encounter-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_5ff93fb2b158eae4d2816ba11120dd8a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Action Value Estimation in Markov Decision Processes","order":16,"contentType":"PROBLEM","contentSlug":"mdp-action-value-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7d7a16f03e4f637155c42ca0cd63cfc8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Multi-Step Value Estimation","order":17,"contentType":"PROBLEM","contentSlug":"multi-step-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d56a313b2117be8b78f80437090e870d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"On-Policy Temporal Difference Learning for Action-Value Estimation","order":18,"contentType":"PROBLEM","contentSlug":"on-policy-temporal-difference-learning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_aeca24f3c0a7af44a85a1eaaf475e981","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Preference-Based Action Selector with Softmax Probabilities","order":19,"contentType":"PROBLEM","contentSlug":"preference-based-action-selector","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_fb601350b7378f76abdb4c82a44aa974","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Recency Weighted Value Estimate","order":20,"contentType":"PROBLEM","contentSlug":"recency-weighted-value-estimate","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_ecff14f5ea50e956a1634213f48bf34d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"State-Value Function Bellman Update","order":21,"contentType":"PROBLEM","contentSlug":"state-value-bellman-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_35c0c9142b9f15c8292c4ba4fe528e93","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Temporal Difference Value Estimator for Sequential Decision Problems","order":22,"contentType":"PROBLEM","contentSlug":"temporal-difference-value-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8af275239547b1851f9cf7cd84dace10","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Unbiased Policy Gradient Objective Computation","order":23,"contentType":"PROBLEM","contentSlug":"unbiased-policy-gradient-objective","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_95be1280288166c72d82b11f6ce127f6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Coin Flip Wealth Strategy Optimizer","order":24,"contentType":"PROBLEM","contentSlug":"coin-flip-wealth-strategy-optimizer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7362f58c845d170d1319c221f248dd39","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Group Policy Optimization Objective","order":25,"contentType":"PROBLEM","contentSlug":"group-policy-optimization-objective","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_8e1df7f5e4a53975416749242a3f633c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Policy Gradient Computation via REINFORCE","order":26,"contentType":"PROBLEM","contentSlug":"reinforce-policy-gradient-estimator","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_02a6f63c7c935e11375b4f2dc00ad57a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Sequence-Level Policy Gradient Objective","order":27,"contentType":"PROBLEM","contentSlug":"sequence-level-policy-gradient-objective","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_2a9a284060bc9d603c8552e64abc3a28","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Simulation-Based Game Tree Exploration","order":28,"contentType":"PROBLEM","contentSlug":"simulation-based-game-tree-exploration","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_eebbbc6330ca399cc0288e887f3f47cb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"TD(λ) Value Estimation with Eligibility Traces","order":29,"contentType":"PROBLEM","contentSlug":"temporal-difference-lambda-eligibility","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_d676092bad7018bc9087a3c1dc45898c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_9dcee77fc280f1ed22c83796b6838ff5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Average Absolute Deviation Score","order":2,"contentType":"VISUALIZER","contentSlug":"average-absolute-deviation-score","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7aa87a6350f3ca4004e7befd673e8396","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Average Absolute Deviation Score","order":3,"contentType":"PROBLEM","contentSlug":"average-absolute-deviation-score","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_b711c804876abba08aefb8a9a32de368","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Batch Inference Health Monitor","order":4,"contentType":"PROBLEM","contentSlug":"batch-inference-health-monitor","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_f8967d30618aa27cf8dae7f82c668d11","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classification Outcome Matrix","order":5,"contentType":"PROBLEM","contentSlug":"binary-classification-outcome-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_609e5ff5433bc9026086b3dd5f203e6c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Overlap Coefficient Calculation","order":6,"contentType":"PROBLEM","contentSlug":"binary-overlap-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3ef5423ef389c3590c5fed992335e132","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Set Similarity Coefficient","order":7,"contentType":"PROBLEM","contentSlug":"binary-set-similarity-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_7b2de8a086a3ee8cccab1fe179e9079e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Accuracy Metric","order":8,"contentType":"PROBLEM","contentSlug":"classification-accuracy-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_45f5ae8a620787e53289fef3963d19aa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Coefficient of Determination Calculator","order":9,"contentType":"PROBLEM","contentSlug":"coefficient-of-determination-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_70f57f04de358ec583f370b4f10e40bf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Conditional Probability Estimation from Categorical Observations","order":10,"contentType":"PROBLEM","contentSlug":"conditional-probability-estimation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_9deb5cc02273303750cb2e58625f15b2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Statistical Divergence Between Probability Distributions","order":11,"contentType":"PROBLEM","contentSlug":"distribution-divergence-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_cfe4c8701ae55e6033bd0d0bb9daad52","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Computing Gini Impurity for Classification Labels","order":12,"contentType":"PROBLEM","contentSlug":"gini-impurity-calculation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z"},{"id":"seed_3971d49ba131dbdc0149ba63531a4f5b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Harmonic Balance Metric for Binary Classification","order":13,"contentType":"PROBLEM","contentSlug":"harmonic-balance-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_9207dc59de2264fe08755a98054ebf4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Weighted Harmonic Mean of Precision and Recall","order":14,"contentType":"PROBLEM","contentSlug":"harmonic-precision-recall-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_25ac20a8bb9ff19c9a5d4e7105cee135","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Inference Performance Metrics Calculator","order":15,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_244b3c5ba5c392a7df31651aed18a0b8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Language Model Perplexity Score","order":16,"contentType":"PROBLEM","contentSlug":"language-model-perplexity-score","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_ee227ad28c7cc42b95b1b2fc11c8a634","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Model Fit Diagnosis: Identifying Generalization Issues","order":17,"contentType":"PROBLEM","contentSlug":"model-fit-diagnosis","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_a24bc8b7ed87ef5cd7f075a4d491c02d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Compute Service Level Agreement Metrics for ML Inference Endpoints","order":18,"contentType":"PROBLEM","contentSlug":"model-service-sla-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_c64f7cb2098b117180b55ef5909a1905","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Precision Evaluation Metric for Binary Classification","order":19,"contentType":"PROBLEM","contentSlug":"precision-evaluation-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_86278bda4af7b507dd31acbf55ab9f42","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Prediction Error Magnitude Calculator","order":20,"contentType":"PROBLEM","contentSlug":"prediction-error-magnitude","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_bc47c21ceb3d43a6df32f0bff5c5eb9d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Sensitivity Score for Binary Classifier","order":21,"contentType":"PROBLEM","contentSlug":"sensitivity-score-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_0d515fcb59707c890ddc6176e5228278","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Service Latency Percentile Metrics","order":22,"contentType":"PROBLEM","contentSlug":"service-latency-percentile-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_696a2bdbb7fdccca1e480b888b493200","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Text Accuracy Evaluator with Normalization","order":23,"contentType":"PROBLEM","contentSlug":"text-accuracy-evaluator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_00ba10c9be865a58e4e1782e1f9022b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Vector Angular Similarity Measure","order":24,"contentType":"PROBLEM","contentSlug":"vector-angular-similarity-measure","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_c679d53ca0e7a542ed486e0212f90213","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classifier Evaluation Suite","order":25,"contentType":"PROBLEM","contentSlug":"binary-classifier-evaluation-suite","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_b006e4f1825efddd2ca9c4770518309c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classifier Performance Curve","order":26,"contentType":"PROBLEM","contentSlug":"binary-classifier-performance-curve","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_8ad0a9155d2470ff78ce257d9701966c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Feature Probabilistic Classifier","order":27,"contentType":"PROBLEM","contentSlug":"binary-feature-probabilistic-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_2a72e24b11adbbe8f488f75690dfed8f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Canary Deployment Health Analysis for ML Model Rollouts","order":28,"contentType":"PROBLEM","contentSlug":"canary-deployment-health-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_617f68e744814a20f916e46a15cdc9d7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Sample Mean Convergence to Gaussian Distribution","order":29,"contentType":"PROBLEM","contentSlug":"central-limit-theorem-simulation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_790f9bb78d594a266e50f14923adf07b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Outcome Matrix Builder","order":30,"contentType":"PROBLEM","contentSlug":"classification-outcome-matrix-builder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_298ba9aade73fbb7373712161d230a13","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Cluster Cohesion Coefficient for Clustering Quality Assessment","order":31,"contentType":"PROBLEM","contentSlug":"cluster-cohesion-coefficient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_057cf133aa54e62b9a10cb03bc155e73","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Code Output Validator for Automated Assessment","order":32,"contentType":"PROBLEM","contentSlug":"code-output-validator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_d83e33ef8a65572febe8f612ab7eaded","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Data Partitioning with Cross-Validation Splits","order":33,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_d92b0d0a435c6a5681e182122cc5e694","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Dataset Integrity Metrics for Production ML Systems","order":34,"contentType":"PROBLEM","contentSlug":"dataset-integrity-metrics","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_e0cff58f1257bdd529d2430fa4a6fed8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Distribution Stability Analysis Using Stability Divergence Index","order":35,"contentType":"PROBLEM","contentSlug":"distribution-stability-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_2cb719335d74c8e6d1024c50834be547","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Inference Request Batching","order":36,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_1b0e83d4d4b81d1463a23f5d576f8a27","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"LaTeX Delimited Answer Extraction","order":37,"contentType":"PROBLEM","contentSlug":"latex-delimited-answer-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_f1fe44c115984737f090efb35b79f5f9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Mathematical Expression Equivalence Validator","order":38,"contentType":"PROBLEM","contentSlug":"mathematical-expression-equivalence-validator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_70d9f8ebb3e65685c01cf122b9a9d1eb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classification Quality Score Using Matthews Correlation","order":39,"contentType":"PROBLEM","contentSlug":"matthews-correlation-coefficient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_206bf5d2e2eb148c585bc5981b29f3bb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Multi-Evaluator Quality Assessment Score","order":40,"contentType":"PROBLEM","contentSlug":"multi-evaluator-quality-assessment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_247324f5264af1d5a45f7c3d3610e872","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Multiple Choice Log-Probability Evaluation","order":41,"contentType":"PROBLEM","contentSlug":"multiple-choice-log-probability-evaluation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_ee87874da7e8b9d0c93bebcc883800d0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Pairwise Ranking Score Adjustment for Competitive Models","order":42,"contentType":"PROBLEM","contentSlug":"pairwise-ranking-score-adjustment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_c272453b5d2c4118b25ddbedc6777dad","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Pairwise Ranking Strength Estimator","order":43,"contentType":"PROBLEM","contentSlug":"pairwise-ranking-strength-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_1d669755173e1c884281d68b2361eb05","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Population Mean Confidence Estimator","order":44,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_7a6a5e3aba0fe03692f99ef9952f827f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Posterior Parameter Estimation with Conjugate Prior","order":45,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_3d36411b86ef1db8a500c42b53203d0d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Precision-Recall Curve for Binary Classification","order":46,"contentType":"PROBLEM","contentSlug":"precision-recall-curve-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_d60565d52a857e179a5e994d0ba7fcb3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Model Prediction Distribution Drift Detector","order":47,"contentType":"PROBLEM","contentSlug":"prediction-distribution-monitoring","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_2f8aa1abc43b9af476c541dd05c20afe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Probabilistic Gaussian Classifier","order":48,"contentType":"PROBLEM","contentSlug":"probabilistic-gaussian-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_b0ae48e1f5413845019a40ddae343c76","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Probability Calibration Assessment","order":49,"contentType":"PROBLEM","contentSlug":"probability-calibration-assessment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_85968379bbe6f46ee6d54c5eaf401b96","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Proximity-Based Neighbor Retrieval","order":50,"contentType":"PROBLEM","contentSlug":"proximity-based-neighbor-retrieval","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_58735bc79c28646ee74eb1bef15995c0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Receiver Operating Characteristic Curve Area Computation","order":51,"contentType":"PROBLEM","contentSlug":"receiver-operating-characteristic-area","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_ae2b3e511cd9550cf02edb2a4f3a308b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Symmetric 8-Bit Integer Quantization","order":52,"contentType":"PROBLEM","contentSlug":"symmetric-8bit-integer-quantization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_570bf4d7a6920c611b8071c5bc335042","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"N-Gram Precision Score for Text Generation Evaluation","order":53,"contentType":"PROBLEM","contentSlug":"text-generation-quality-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_4543254e3612e9220968e6dac41e462a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Translation Alignment Metric for Quality Assessment","order":54,"contentType":"PROBLEM","contentSlug":"translation-alignment-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_5709e17c375dfaada45ac8b7d35b9594","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Unigram Overlap Evaluation Metric","order":55,"contentType":"PROBLEM","contentSlug":"unigram-overlap-evaluation-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_83c91368fde51847eb9c7d380d514ce0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Variance Ratio Criterion for Cluster Quality Assessment","order":56,"contentType":"PROBLEM","contentSlug":"variance-ratio-clustering-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_8b640f246b265f3705dabaeb2f785232","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Weighted Model Response Evaluator","order":57,"contentType":"PROBLEM","contentSlug":"weighted-model-response-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_c888b913f332e0c96f9d76e93296fccd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Approximate Posterior Quality Metric via Monte Carlo Sampling","order":58,"contentType":"PROBLEM","contentSlug":"variational-bound-estimation","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_4ed553fb5a4ee697e73c7bc8a8413602","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Workflow Dependency Scheduler with Critical Path Analysis","order":59,"contentType":"PROBLEM","contentSlug":"workflow-dependency-scheduler-critical-path","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_1cf3ca25b4d85e36ac916fe9bf291c5c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_aade27e9f399c96b30b15498a3677c02","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Single Word Frequency Likelihood","order":2,"contentType":"VISUALIZER","contentSlug":"single-word-frequency-likelihood","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_2b5dfacb2bc14598853faa7c10b40eb2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Single Word Frequency Likelihood","order":3,"contentType":"PROBLEM","contentSlug":"single-word-frequency-likelihood","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_cbd9fbefb3f64bf7ef0dfad6c77ccfeb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Association Strength Metric Between Events","order":4,"contentType":"PROBLEM","contentSlug":"association-strength-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_3bf818df30b9d1a285616a8d21b04ff8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Optimal String Edit Metric","order":5,"contentType":"PROBLEM","contentSlug":"optimal-string-edit-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_b244395d84a26e25343469f65aa46024","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Probabilistic Document Relevance Scorer (BM25)","order":6,"contentType":"PROBLEM","contentSlug":"probabilistic-relevance-scorer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_a54be1839791d24df220d15a475d56a0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Computing Document Term Relevance Scores","order":7,"contentType":"PROBLEM","contentSlug":"term-frequency-inverse-document-frequency","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_01e679cf3875844c326b1d46cdda1c61","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_1e619df8a6869c66c86dffd02326e6ca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Portfolio Risk Variance Computation","order":2,"contentType":"VISUALIZER","contentSlug":"portfolio-risk-variance-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_26f2d313e7d6e73a7337ca91d76256a1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Portfolio Risk Variance Computation","order":3,"contentType":"PROBLEM","contentSlug":"portfolio-risk-variance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_0ddbdd932939ea21de91ea9e4554afef","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Monetary Volume Aggregated Bars","order":4,"contentType":"PROBLEM","contentSlug":"monetary-volume-aggregated-bars","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_e37069aafdb57e221cd2889f3b150ffa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Transaction-Based Bar Aggregation","order":5,"contentType":"PROBLEM","contentSlug":"transaction-based-bar-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_02ca952bebd85261b44ff1b91fbc85b8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Volume-Based Candlestick Aggregation","order":6,"contentType":"PROBLEM","contentSlug":"volume-based-candlestick-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_89f740cbcf7a1aec02b5744bb0d900ce","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_369219c088ffe96e3d00574dd39e4a46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"Frequent Pattern Discovery in Transaction Data","order":2,"contentType":"VISUALIZER","contentSlug":"frequent-pattern-discovery","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"},{"id":"seed_c2975623377895773b4dd6b3dfafa2e7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"Frequent Pattern Discovery in Transaction Data","order":3,"contentType":"PROBLEM","contentSlug":"frequent-pattern-discovery","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z"}],"sections":[{"title":"Linear algebra","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","$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","$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","$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","$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","$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"]},{"title":"Probability and statistics","order":2,"modules":["$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","$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","$46:props:state:queries:0:state:data:json:modules:43","$46:props:state:queries:0:state:data:json:modules:44","$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","$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","$46:props:state:queries:0:state:data:json:modules:58","$46:props:state:queries:0:state:data:json:modules:59","$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":"Calculus and optimization","order":3,"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","$46:props:state:queries:0:state:data:json:modules:71","$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","$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","$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","$46:props:state:queries:0:state:data:json:modules:93","$46:props:state:queries:0:state:data:json:modules:94","$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","$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","$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","$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","$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","$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","$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":"Feature preparation","order":4,"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","$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","$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","$46:props:state:queries:0:state:data:json:modules:145","$46:props:state:queries:0:state:data:json:modules:146"]},{"title":"Classical machine learning","order":5,"modules":["$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","$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","$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","$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"]},{"title":"Neural networks and efficient training","order":6,"modules":["$46:props:state:queries:0:state:data:json:modules:165","$46:props:state:queries:0:state:data:json:modules:166","$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","$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","$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","$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","$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"]},{"title":"Convolution and computer vision","order":7,"modules":["$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","$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","$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","$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","$46:props:state:queries:0:state:data:json:modules:212"]},{"title":"Sequence models","order":8,"modules":["$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","$46:props:state:queries:0:state:data:json:modules:216","$46:props:state:queries:0:state:data:json:modules:217"]},{"title":"Attention and transformers","order":9,"modules":["$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","$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","$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","$46:props:state:queries:0:state:data:json:modules:235"]},{"title":"Generative models","order":10,"modules":["$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":"Reinforcement learning","order":11,"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","$46:props:state:queries:0:state:data:json:modules:250","$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","$46:props:state:queries:0:state:data:json:modules:256","$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","$46:props:state:queries:0:state:data:json:modules:262","$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","$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"]},{"title":"ML serving and evaluation","order":12,"modules":["$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","$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","$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","$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","$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","$46:props:state:queries:0:state:data:json:modules:302","$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","$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","$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","$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","$46:props:state:queries:0:state:data:json:modules:328","$46:props:state:queries:0:state:data:json:modules:329","$46:props:state:queries:0:state:data:json:modules:330"]},{"title":"Text and information retrieval","order":13,"modules":["$46:props:state:queries:0:state:data:json:modules:331","$46:props:state:queries:0:state:data:json:modules:332","$46:props:state:queries:0:state:data:json:modules:333","$46:props:state:queries:0:state:data:json:modules:334","$46:props:state:queries:0:state:data:json:modules:335","$46:props:state:queries:0:state:data:json:modules:336","$46:props:state:queries:0:state:data:json:modules:337"]},{"title":"Financial time series","order":14,"modules":["$46:props:state:queries:0:state:data:json:modules:338","$46:props:state:queries:0:state:data:json:modules:339","$46:props:state:queries:0:state:data:json:modules:340","$46:props:state:queries:0:state:data:json:modules:341","$46:props:state:queries:0:state:data:json:modules:342","$46:props:state:queries:0:state:data:json:modules:343"]},{"title":"Additional numerical and ML techniques","order":15,"modules":["$46:props:state:queries:0:state:data:json:modules:344","$46:props:state:queries:0:state:data:json:modules:345","$46:props:state:queries:0:state:data:json:modules:346"]}]},"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"],"modules.328.createdAt":["Date"],"modules.328.updatedAt":["Date"],"modules.329.createdAt":["Date"],"modules.329.updatedAt":["Date"],"modules.330.createdAt":["Date"],"modules.330.updatedAt":["Date"],"modules.331.createdAt":["Date"],"modules.331.updatedAt":["Date"],"modules.332.createdAt":["Date"],"modules.332.updatedAt":["Date"],"modules.333.createdAt":["Date"],"modules.333.updatedAt":["Date"],"modules.334.createdAt":["Date"],"modules.334.updatedAt":["Date"],"modules.335.createdAt":["Date"],"modules.335.updatedAt":["Date"],"modules.336.createdAt":["Date"],"modules.336.updatedAt":["Date"],"modules.337.createdAt":["Date"],"modules.337.updatedAt":["Date"],"modules.338.createdAt":["Date"],"modules.338.updatedAt":["Date"],"modules.339.createdAt":["Date"],"modules.339.updatedAt":["Date"],"modules.340.createdAt":["Date"],"modules.340.updatedAt":["Date"],"modules.341.createdAt":["Date"],"modules.341.updatedAt":["Date"],"modules.342.createdAt":["Date"],"modules.342.updatedAt":["Date"],"modules.343.createdAt":["Date"],"modules.343.updatedAt":["Date"],"modules.344.createdAt":["Date"],"modules.344.updatedAt":["Date"],"modules.345.createdAt":["Date"],"modules.345.updatedAt":["Date"],"modules.346.createdAt":["Date"],"modules.346.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.0.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.4.createdAt","sections.0.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.4.updatedAt","sections.0.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.5.createdAt","sections.0.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.5.updatedAt","sections.0.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.6.createdAt","sections.0.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.6.updatedAt","sections.0.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.7.createdAt","sections.0.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.7.updatedAt","sections.0.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.8.createdAt","sections.0.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.8.updatedAt","sections.0.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.9.createdAt","sections.0.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.9.updatedAt","sections.0.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.10.createdAt","sections.0.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.10.updatedAt","sections.0.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.11.createdAt","sections.0.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.11.updatedAt","sections.0.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.12.createdAt","sections.0.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.12.updatedAt","sections.0.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.13.createdAt","sections.0.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.13.updatedAt","sections.0.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.14.createdAt","sections.0.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.14.updatedAt","sections.0.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.15.createdAt","sections.0.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.15.updatedAt","sections.0.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.16.createdAt","sections.0.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.16.updatedAt","sections.0.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.17.createdAt","sections.0.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.17.updatedAt","sections.0.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.18.createdAt","sections.0.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.18.updatedAt","sections.0.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.19.createdAt","sections.0.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.19.updatedAt","sections.0.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.20.createdAt","sections.0.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.20.updatedAt","sections.0.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.21.createdAt","sections.0.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.21.updatedAt","sections.0.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.22.createdAt","sections.0.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.22.updatedAt","sections.0.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.23.createdAt","sections.0.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.23.updatedAt","sections.0.modules.24.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.24.createdAt","sections.0.modules.24.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.24.updatedAt","sections.0.modules.25.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.25.createdAt","sections.0.modules.25.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.25.updatedAt","sections.0.modules.26.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.26.createdAt","sections.0.modules.26.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.26.updatedAt","sections.0.modules.27.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.27.createdAt","sections.0.modules.27.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.27.updatedAt","sections.0.modules.28.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.28.createdAt","sections.0.modules.28.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.28.updatedAt","sections.0.modules.29.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.29.createdAt","sections.0.modules.29.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.29.updatedAt","sections.0.modules.30.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.30.createdAt","sections.0.modules.30.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.30.updatedAt","sections.0.modules.31.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.31.createdAt","sections.0.modules.31.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.31.updatedAt","sections.1.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.32.createdAt","sections.1.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.32.updatedAt","sections.1.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.33.createdAt","sections.1.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.33.updatedAt","sections.1.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.34.createdAt","sections.1.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.34.updatedAt","sections.1.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.35.createdAt","sections.1.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.35.updatedAt","sections.1.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.36.createdAt","sections.1.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.36.updatedAt","sections.1.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.37.createdAt","sections.1.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.37.updatedAt","sections.1.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.38.createdAt","sections.1.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.38.updatedAt","sections.1.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.39.createdAt","sections.1.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.39.updatedAt","sections.1.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.40.createdAt","sections.1.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.40.updatedAt","sections.1.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.41.createdAt","sections.1.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.41.updatedAt","sections.1.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.42.createdAt","sections.1.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.42.updatedAt","sections.1.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.43.createdAt","sections.1.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.43.updatedAt","sections.1.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.44.createdAt","sections.1.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.44.updatedAt","sections.1.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.45.createdAt","sections.1.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.45.updatedAt","sections.1.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.46.createdAt","sections.1.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.46.updatedAt","sections.1.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.47.createdAt","sections.1.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.47.updatedAt","sections.1.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.48.createdAt","sections.1.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.48.updatedAt","sections.1.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.49.createdAt","sections.1.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.49.updatedAt","sections.1.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.50.createdAt","sections.1.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.50.updatedAt","sections.1.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.51.createdAt","sections.1.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.51.updatedAt","sections.1.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.52.createdAt","sections.1.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.52.updatedAt","sections.1.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.53.createdAt","sections.1.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.53.updatedAt","sections.1.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.54.createdAt","sections.1.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.54.updatedAt","sections.1.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.55.createdAt","sections.1.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.55.updatedAt","sections.1.modules.24.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.56.createdAt","sections.1.modules.24.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.56.updatedAt","sections.1.modules.25.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.57.createdAt","sections.1.modules.25.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.57.updatedAt","sections.1.modules.26.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.58.createdAt","sections.1.modules.26.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.58.updatedAt","sections.1.modules.27.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.59.createdAt","sections.1.modules.27.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.59.updatedAt","sections.1.modules.28.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.60.createdAt","sections.1.modules.28.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.60.updatedAt","sections.1.modules.29.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.61.createdAt","sections.1.modules.29.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.61.updatedAt","sections.1.modules.30.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.62.createdAt","sections.1.modules.30.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.62.updatedAt","sections.1.modules.31.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.63.createdAt","sections.1.modules.31.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.63.updatedAt","sections.1.modules.32.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.64.createdAt","sections.1.modules.32.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.64.updatedAt","sections.2.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.65.createdAt","sections.2.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.65.updatedAt","sections.2.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.66.createdAt","sections.2.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.66.updatedAt","sections.2.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.67.createdAt","sections.2.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.67.updatedAt","sections.2.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.68.createdAt","sections.2.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.68.updatedAt","sections.2.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.69.createdAt","sections.2.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.69.updatedAt","sections.2.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.70.createdAt","sections.2.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.70.updatedAt","sections.2.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.71.createdAt","sections.2.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.71.updatedAt","sections.2.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.72.createdAt","sections.2.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.72.updatedAt","sections.2.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.73.createdAt","sections.2.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.73.updatedAt","sections.2.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.74.createdAt","sections.2.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.74.updatedAt","sections.2.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.75.createdAt","sections.2.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.75.updatedAt","sections.2.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.76.createdAt","sections.2.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.76.updatedAt","sections.2.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.77.createdAt","sections.2.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.77.updatedAt","sections.2.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.78.createdAt","sections.2.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.78.updatedAt","sections.2.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.79.createdAt","sections.2.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.79.updatedAt","sections.2.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.80.createdAt","sections.2.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.80.updatedAt","sections.2.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.81.createdAt","sections.2.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.81.updatedAt","sections.2.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.82.createdAt","sections.2.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.82.updatedAt","sections.2.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.83.createdAt","sections.2.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.83.updatedAt","sections.2.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.84.createdAt","sections.2.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.84.updatedAt","sections.2.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.85.createdAt","sections.2.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.85.updatedAt","sections.2.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.86.createdAt","sections.2.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.86.updatedAt","sections.2.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.87.createdAt","sections.2.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.87.updatedAt","sections.2.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.88.createdAt","sections.2.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.88.updatedAt","sections.2.modules.24.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.89.createdAt","sections.2.modules.24.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.89.updatedAt","sections.2.modules.25.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.90.createdAt","sections.2.modules.25.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.90.updatedAt","sections.2.modules.26.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.91.createdAt","sections.2.modules.26.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.91.updatedAt","sections.2.modules.27.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.92.createdAt","sections.2.modules.27.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.92.updatedAt","sections.2.modules.28.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.93.createdAt","sections.2.modules.28.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.93.updatedAt","sections.2.modules.29.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.94.createdAt","sections.2.modules.29.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.94.updatedAt","sections.2.modules.30.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.95.createdAt","sections.2.modules.30.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.95.updatedAt","sections.2.modules.31.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.96.createdAt","sections.2.modules.31.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.96.updatedAt","sections.2.modules.32.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.97.createdAt","sections.2.modules.32.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.97.updatedAt","sections.2.modules.33.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.98.createdAt","sections.2.modules.33.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.98.updatedAt","sections.2.modules.34.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.99.createdAt","sections.2.modules.34.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.99.updatedAt","sections.2.modules.35.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.100.createdAt","sections.2.modules.35.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.100.updatedAt","sections.2.modules.36.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.101.createdAt","sections.2.modules.36.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.101.updatedAt","sections.2.modules.37.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.102.createdAt","sections.2.modules.37.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.102.updatedAt","sections.2.modules.38.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.103.createdAt","sections.2.modules.38.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.103.updatedAt","sections.2.modules.39.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.104.createdAt","sections.2.modules.39.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.104.updatedAt","sections.2.modules.40.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.105.createdAt","sections.2.modules.40.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.105.updatedAt","sections.2.modules.41.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.106.createdAt","sections.2.modules.41.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.106.updatedAt","sections.2.modules.42.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.107.createdAt","sections.2.modules.42.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.107.updatedAt","sections.2.modules.43.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.108.createdAt","sections.2.modules.43.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.108.updatedAt","sections.2.modules.44.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.109.createdAt","sections.2.modules.44.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.109.updatedAt","sections.2.modules.45.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.110.createdAt","sections.2.modules.45.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.110.updatedAt","sections.2.modules.46.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.111.createdAt","sections.2.modules.46.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.111.updatedAt","sections.2.modules.47.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.112.createdAt","sections.2.modules.47.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.112.updatedAt","sections.2.modules.48.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.113.createdAt","sections.2.modules.48.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.113.updatedAt","sections.2.modules.49.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.114.createdAt","sections.2.modules.49.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.114.updatedAt","sections.2.modules.50.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.115.createdAt","sections.2.modules.50.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.115.updatedAt","sections.2.modules.51.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.116.createdAt","sections.2.modules.51.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.116.updatedAt","sections.2.modules.52.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.117.createdAt","sections.2.modules.52.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.117.updatedAt","sections.2.modules.53.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.118.createdAt","sections.2.modules.53.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.118.updatedAt","sections.2.modules.54.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.119.createdAt","sections.2.modules.54.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.119.updatedAt","sections.2.modules.55.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.120.createdAt","sections.2.modules.55.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.120.updatedAt","sections.2.modules.56.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.121.createdAt","sections.2.modules.56.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.121.updatedAt","sections.2.modules.57.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.122.createdAt","sections.2.modules.57.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.122.updatedAt","sections.2.modules.58.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.123.createdAt","sections.2.modules.58.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.123.updatedAt","sections.2.modules.59.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.124.createdAt","sections.2.modules.59.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.124.updatedAt","sections.2.modules.60.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.125.createdAt","sections.2.modules.60.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.125.updatedAt","sections.2.modules.61.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.126.createdAt","sections.2.modules.61.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.126.updatedAt","sections.2.modules.62.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.127.createdAt","sections.2.modules.62.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.127.updatedAt","sections.2.modules.63.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.128.createdAt","sections.2.modules.63.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.128.updatedAt","sections.2.modules.64.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.129.createdAt","sections.2.modules.64.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.129.updatedAt","sections.2.modules.65.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.130.createdAt","sections.2.modules.65.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.130.updatedAt","sections.2.modules.66.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.131.createdAt","sections.2.modules.66.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.131.updatedAt","sections.3.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.132.createdAt","sections.3.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.132.updatedAt","sections.3.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.133.createdAt","sections.3.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.133.updatedAt","sections.3.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.134.createdAt","sections.3.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.134.updatedAt","sections.3.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.135.createdAt","sections.3.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.135.updatedAt","sections.3.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.136.createdAt","sections.3.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.136.updatedAt","sections.3.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.137.createdAt","sections.3.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.137.updatedAt","sections.3.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.138.createdAt","sections.3.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.138.updatedAt","sections.3.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.139.createdAt","sections.3.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.139.updatedAt","sections.3.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.140.createdAt","sections.3.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.140.updatedAt","sections.3.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.141.createdAt","sections.3.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.141.updatedAt","sections.3.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.142.createdAt","sections.3.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.142.updatedAt","sections.3.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.143.createdAt","sections.3.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.143.updatedAt","sections.3.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.144.createdAt","sections.3.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.144.updatedAt","sections.3.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.145.createdAt","sections.3.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.145.updatedAt","sections.3.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.146.createdAt","sections.3.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.146.updatedAt","sections.4.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.147.createdAt","sections.4.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.147.updatedAt","sections.4.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.148.createdAt","sections.4.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.148.updatedAt","sections.4.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.149.createdAt","sections.4.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.149.updatedAt","sections.4.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.150.createdAt","sections.4.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.150.updatedAt","sections.4.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.151.createdAt","sections.4.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.151.updatedAt","sections.4.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.152.createdAt","sections.4.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.152.updatedAt","sections.4.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.153.createdAt","sections.4.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.153.updatedAt","sections.4.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.154.createdAt","sections.4.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.154.updatedAt","sections.4.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.155.createdAt","sections.4.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.155.updatedAt","sections.4.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.156.createdAt","sections.4.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.156.updatedAt","sections.4.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.157.createdAt","sections.4.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.157.updatedAt","sections.4.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.158.createdAt","sections.4.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.158.updatedAt","sections.4.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.159.createdAt","sections.4.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.159.updatedAt","sections.4.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.160.createdAt","sections.4.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.160.updatedAt","sections.4.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.161.createdAt","sections.4.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.161.updatedAt","sections.4.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.162.createdAt","sections.4.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.162.updatedAt","sections.4.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.163.createdAt","sections.4.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.163.updatedAt","sections.4.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.164.createdAt","sections.4.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.164.updatedAt","sections.5.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.165.createdAt","sections.5.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.165.updatedAt","sections.5.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.166.createdAt","sections.5.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.166.updatedAt","sections.5.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.167.createdAt","sections.5.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.167.updatedAt","sections.5.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.168.createdAt","sections.5.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.168.updatedAt","sections.5.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.169.createdAt","sections.5.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.169.updatedAt","sections.5.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.170.createdAt","sections.5.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.170.updatedAt","sections.5.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.171.createdAt","sections.5.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.171.updatedAt","sections.5.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.172.createdAt","sections.5.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.172.updatedAt","sections.5.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.173.createdAt","sections.5.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.173.updatedAt","sections.5.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.174.createdAt","sections.5.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.174.updatedAt","sections.5.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.175.createdAt","sections.5.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.175.updatedAt","sections.5.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.176.createdAt","sections.5.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.176.updatedAt","sections.5.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.177.createdAt","sections.5.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.177.updatedAt","sections.5.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.178.createdAt","sections.5.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.178.updatedAt","sections.5.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.179.createdAt","sections.5.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.179.updatedAt","sections.5.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.180.createdAt","sections.5.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.180.updatedAt","sections.5.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.181.createdAt","sections.5.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.181.updatedAt","sections.5.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.182.createdAt","sections.5.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.182.updatedAt","sections.5.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.183.createdAt","sections.5.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.183.updatedAt","sections.5.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.184.createdAt","sections.5.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.184.updatedAt","sections.5.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.185.createdAt","sections.5.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.185.updatedAt","sections.5.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.186.createdAt","sections.5.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.186.updatedAt","sections.5.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.187.createdAt","sections.5.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.187.updatedAt","sections.5.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.188.createdAt","sections.5.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.188.updatedAt","sections.6.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.189.createdAt","sections.6.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.189.updatedAt","sections.6.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.190.createdAt","sections.6.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.190.updatedAt","sections.6.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.191.createdAt","sections.6.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.191.updatedAt","sections.6.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.192.createdAt","sections.6.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.192.updatedAt","sections.6.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.193.createdAt","sections.6.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.193.updatedAt","sections.6.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.194.createdAt","sections.6.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.194.updatedAt","sections.6.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.195.createdAt","sections.6.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.195.updatedAt","sections.6.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.196.createdAt","sections.6.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.196.updatedAt","sections.6.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.197.createdAt","sections.6.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.197.updatedAt","sections.6.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.198.createdAt","sections.6.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.198.updatedAt","sections.6.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.199.createdAt","sections.6.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.199.updatedAt","sections.6.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.200.createdAt","sections.6.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.200.updatedAt","sections.6.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.201.createdAt","sections.6.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.201.updatedAt","sections.6.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.202.createdAt","sections.6.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.202.updatedAt","sections.6.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.203.createdAt","sections.6.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.203.updatedAt","sections.6.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.204.createdAt","sections.6.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.204.updatedAt","sections.6.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.205.createdAt","sections.6.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.205.updatedAt","sections.6.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.206.createdAt","sections.6.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.206.updatedAt","sections.6.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.207.createdAt","sections.6.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.207.updatedAt","sections.6.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.208.createdAt","sections.6.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.208.updatedAt","sections.6.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.209.createdAt","sections.6.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.209.updatedAt","sections.6.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.210.createdAt","sections.6.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.210.updatedAt","sections.6.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.211.createdAt","sections.6.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.211.updatedAt","sections.6.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.212.createdAt","sections.6.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.212.updatedAt","sections.7.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.213.createdAt","sections.7.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.213.updatedAt","sections.7.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.214.createdAt","sections.7.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.214.updatedAt","sections.7.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.215.createdAt","sections.7.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.215.updatedAt","sections.7.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.216.createdAt","sections.7.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.216.updatedAt","sections.7.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.217.createdAt","sections.7.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.217.updatedAt","sections.8.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.218.createdAt","sections.8.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.218.updatedAt","sections.8.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.219.createdAt","sections.8.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.219.updatedAt","sections.8.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.220.createdAt","sections.8.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.220.updatedAt","sections.8.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.221.createdAt","sections.8.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.221.updatedAt","sections.8.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.222.createdAt","sections.8.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.222.updatedAt","sections.8.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.223.createdAt","sections.8.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.223.updatedAt","sections.8.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.224.createdAt","sections.8.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.224.updatedAt","sections.8.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.225.createdAt","sections.8.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.225.updatedAt","sections.8.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.226.createdAt","sections.8.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.226.updatedAt","sections.8.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.227.createdAt","sections.8.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.227.updatedAt","sections.8.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.228.createdAt","sections.8.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.228.updatedAt","sections.8.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.229.createdAt","sections.8.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.229.updatedAt","sections.8.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.230.createdAt","sections.8.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.230.updatedAt","sections.8.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.231.createdAt","sections.8.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.231.updatedAt","sections.8.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.232.createdAt","sections.8.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.232.updatedAt","sections.8.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.233.createdAt","sections.8.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.233.updatedAt","sections.8.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.234.createdAt","sections.8.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.234.updatedAt","sections.8.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.235.createdAt","sections.8.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.235.updatedAt","sections.9.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.236.createdAt","sections.9.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.236.updatedAt","sections.9.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.237.createdAt","sections.9.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.237.updatedAt","sections.9.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.238.createdAt","sections.9.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.238.updatedAt","sections.9.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.239.createdAt","sections.9.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.239.updatedAt","sections.9.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.240.createdAt","sections.9.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.240.updatedAt","sections.9.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.241.createdAt","sections.9.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.241.updatedAt","sections.9.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.242.createdAt","sections.9.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.242.updatedAt","sections.10.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.243.createdAt","sections.10.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.243.updatedAt","sections.10.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.244.createdAt","sections.10.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.244.updatedAt","sections.10.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.245.createdAt","sections.10.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.245.updatedAt","sections.10.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.246.createdAt","sections.10.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.246.updatedAt","sections.10.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.247.createdAt","sections.10.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.247.updatedAt","sections.10.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.248.createdAt","sections.10.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.248.updatedAt","sections.10.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.249.createdAt","sections.10.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.249.updatedAt","sections.10.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.250.createdAt","sections.10.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.250.updatedAt","sections.10.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.251.createdAt","sections.10.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.251.updatedAt","sections.10.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.252.createdAt","sections.10.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.252.updatedAt","sections.10.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.253.createdAt","sections.10.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.253.updatedAt","sections.10.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.254.createdAt","sections.10.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.254.updatedAt","sections.10.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.255.createdAt","sections.10.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.255.updatedAt","sections.10.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.256.createdAt","sections.10.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.256.updatedAt","sections.10.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.257.createdAt","sections.10.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.257.updatedAt","sections.10.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.258.createdAt","sections.10.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.258.updatedAt","sections.10.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.259.createdAt","sections.10.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.259.updatedAt","sections.10.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.260.createdAt","sections.10.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.260.updatedAt","sections.10.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.261.createdAt","sections.10.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.261.updatedAt","sections.10.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.262.createdAt","sections.10.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.262.updatedAt","sections.10.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.263.createdAt","sections.10.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.263.updatedAt","sections.10.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.264.createdAt","sections.10.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.264.updatedAt","sections.10.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.265.createdAt","sections.10.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.265.updatedAt","sections.10.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.266.createdAt","sections.10.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.266.updatedAt","sections.10.modules.24.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.267.createdAt","sections.10.modules.24.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.267.updatedAt","sections.10.modules.25.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.268.createdAt","sections.10.modules.25.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.268.updatedAt","sections.10.modules.26.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.269.createdAt","sections.10.modules.26.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.269.updatedAt","sections.10.modules.27.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.270.createdAt","sections.10.modules.27.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.270.updatedAt","sections.10.modules.28.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.271.createdAt","sections.10.modules.28.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.271.updatedAt","sections.11.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.272.createdAt","sections.11.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.272.updatedAt","sections.11.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.273.createdAt","sections.11.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.273.updatedAt","sections.11.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.274.createdAt","sections.11.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.274.updatedAt","sections.11.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.275.createdAt","sections.11.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.275.updatedAt","sections.11.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.276.createdAt","sections.11.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.276.updatedAt","sections.11.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.277.createdAt","sections.11.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.277.updatedAt","sections.11.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.278.createdAt","sections.11.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.278.updatedAt","sections.11.modules.7.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.279.createdAt","sections.11.modules.7.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.279.updatedAt","sections.11.modules.8.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.280.createdAt","sections.11.modules.8.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.280.updatedAt","sections.11.modules.9.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.281.createdAt","sections.11.modules.9.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.281.updatedAt","sections.11.modules.10.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.282.createdAt","sections.11.modules.10.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.282.updatedAt","sections.11.modules.11.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.283.createdAt","sections.11.modules.11.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.283.updatedAt","sections.11.modules.12.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.284.createdAt","sections.11.modules.12.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.284.updatedAt","sections.11.modules.13.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.285.createdAt","sections.11.modules.13.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.285.updatedAt","sections.11.modules.14.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.286.createdAt","sections.11.modules.14.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.286.updatedAt","sections.11.modules.15.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.287.createdAt","sections.11.modules.15.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.287.updatedAt","sections.11.modules.16.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.288.createdAt","sections.11.modules.16.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.288.updatedAt","sections.11.modules.17.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.289.createdAt","sections.11.modules.17.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.289.updatedAt","sections.11.modules.18.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.290.createdAt","sections.11.modules.18.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.290.updatedAt","sections.11.modules.19.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.291.createdAt","sections.11.modules.19.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.291.updatedAt","sections.11.modules.20.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.292.createdAt","sections.11.modules.20.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.292.updatedAt","sections.11.modules.21.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.293.createdAt","sections.11.modules.21.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.293.updatedAt","sections.11.modules.22.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.294.createdAt","sections.11.modules.22.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.294.updatedAt","sections.11.modules.23.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.295.createdAt","sections.11.modules.23.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.295.updatedAt","sections.11.modules.24.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.296.createdAt","sections.11.modules.24.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.296.updatedAt","sections.11.modules.25.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.297.createdAt","sections.11.modules.25.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.297.updatedAt","sections.11.modules.26.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.298.createdAt","sections.11.modules.26.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.298.updatedAt","sections.11.modules.27.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.299.createdAt","sections.11.modules.27.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.299.updatedAt","sections.11.modules.28.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.300.createdAt","sections.11.modules.28.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.300.updatedAt","sections.11.modules.29.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.301.createdAt","sections.11.modules.29.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.301.updatedAt","sections.11.modules.30.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.302.createdAt","sections.11.modules.30.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.302.updatedAt","sections.11.modules.31.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.303.createdAt","sections.11.modules.31.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.303.updatedAt","sections.11.modules.32.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.304.createdAt","sections.11.modules.32.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.304.updatedAt","sections.11.modules.33.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.305.createdAt","sections.11.modules.33.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.305.updatedAt","sections.11.modules.34.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.306.createdAt","sections.11.modules.34.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.306.updatedAt","sections.11.modules.35.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.307.createdAt","sections.11.modules.35.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.307.updatedAt","sections.11.modules.36.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.308.createdAt","sections.11.modules.36.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.308.updatedAt","sections.11.modules.37.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.309.createdAt","sections.11.modules.37.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.309.updatedAt","sections.11.modules.38.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.310.createdAt","sections.11.modules.38.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.310.updatedAt","sections.11.modules.39.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.311.createdAt","sections.11.modules.39.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.311.updatedAt","sections.11.modules.40.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.312.createdAt","sections.11.modules.40.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.312.updatedAt","sections.11.modules.41.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.313.createdAt","sections.11.modules.41.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.313.updatedAt","sections.11.modules.42.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.314.createdAt","sections.11.modules.42.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.314.updatedAt","sections.11.modules.43.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.315.createdAt","sections.11.modules.43.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.315.updatedAt","sections.11.modules.44.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.316.createdAt","sections.11.modules.44.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.316.updatedAt","sections.11.modules.45.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.317.createdAt","sections.11.modules.45.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.317.updatedAt","sections.11.modules.46.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.318.createdAt","sections.11.modules.46.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.318.updatedAt","sections.11.modules.47.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.319.createdAt","sections.11.modules.47.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.319.updatedAt","sections.11.modules.48.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.320.createdAt","sections.11.modules.48.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.320.updatedAt","sections.11.modules.49.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.321.createdAt","sections.11.modules.49.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.321.updatedAt","sections.11.modules.50.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.322.createdAt","sections.11.modules.50.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.322.updatedAt","sections.11.modules.51.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.323.createdAt","sections.11.modules.51.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.323.updatedAt","sections.11.modules.52.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.324.createdAt","sections.11.modules.52.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.324.updatedAt","sections.11.modules.53.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.325.createdAt","sections.11.modules.53.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.325.updatedAt","sections.11.modules.54.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.326.createdAt","sections.11.modules.54.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.326.updatedAt","sections.11.modules.55.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.327.createdAt","sections.11.modules.55.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.327.updatedAt","sections.11.modules.56.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.328.createdAt","sections.11.modules.56.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.328.updatedAt","sections.11.modules.57.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.329.createdAt","sections.11.modules.57.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.329.updatedAt","sections.11.modules.58.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.330.createdAt","sections.11.modules.58.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.330.updatedAt","sections.12.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.331.createdAt","sections.12.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.331.updatedAt","sections.12.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.332.createdAt","sections.12.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.332.updatedAt","sections.12.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.333.createdAt","sections.12.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.333.updatedAt","sections.12.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.334.createdAt","sections.12.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.334.updatedAt","sections.12.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.335.createdAt","sections.12.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.335.updatedAt","sections.12.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.336.createdAt","sections.12.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.336.updatedAt","sections.12.modules.6.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.337.createdAt","sections.12.modules.6.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.337.updatedAt","sections.13.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.338.createdAt","sections.13.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.338.updatedAt","sections.13.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.339.createdAt","sections.13.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.339.updatedAt","sections.13.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.340.createdAt","sections.13.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.340.updatedAt","sections.13.modules.3.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.341.createdAt","sections.13.modules.3.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.341.updatedAt","sections.13.modules.4.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.342.createdAt","sections.13.modules.4.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.342.updatedAt","sections.13.modules.5.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.343.createdAt","sections.13.modules.5.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.343.updatedAt","sections.14.modules.0.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.344.createdAt","sections.14.modules.0.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.344.updatedAt","sections.14.modules.1.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.345.createdAt","sections.14.modules.1.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.345.updatedAt","sections.14.modules.2.createdAt":"$46:props:state:queries:0:state:data:meta:values:modules.346.createdAt","sections.14.modules.2.updatedAt":"$46:props:state:queries:0:state:data:meta:values:modules.346.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.0.modules.4"],"modules.5":["sections.0.modules.5"],"modules.6":["sections.0.modules.6"],"modules.7":["sections.0.modules.7"],"modules.8":["sections.0.modules.8"],"modules.9":["sections.0.modules.9"],"modules.10":["sections.0.modules.10"],"modules.11":["sections.0.modules.11"],"modules.12":["sections.0.modules.12"],"modules.13":["sections.0.modules.13"],"modules.14":["sections.0.modules.14"],"modules.15":["sections.0.modules.15"],"modules.16":["sections.0.modules.16"],"modules.17":["sections.0.modules.17"],"modules.18":["sections.0.modules.18"],"modules.19":["sections.0.modules.19"],"modules.20":["sections.0.modules.20"],"modules.21":["sections.0.modules.21"],"modules.22":["sections.0.modules.22"],"modules.23":["sections.0.modules.23"],"modules.24":["sections.0.modules.24"],"modules.25":["sections.0.modules.25"],"modules.26":["sections.0.modules.26"],"modules.27":["sections.0.modules.27"],"modules.28":["sections.0.modules.28"],"modules.29":["sections.0.modules.29"],"modules.30":["sections.0.modules.30"],"modules.31":["sections.0.modules.31"],"modules.32":["sections.1.modules.0"],"modules.33":["sections.1.modules.1"],"modules.34":["sections.1.modules.2"],"modules.35":["sections.1.modules.3"],"modules.36":["sections.1.modules.4"],"modules.37":["sections.1.modules.5"],"modules.38":["sections.1.modules.6"],"modules.39":["sections.1.modules.7"],"modules.40":["sections.1.modules.8"],"modules.41":["sections.1.modules.9"],"modules.42":["sections.1.modules.10"],"modules.43":["sections.1.modules.11"],"modules.44":["sections.1.modules.12"],"modules.45":["sections.1.modules.13"],"modules.46":["sections.1.modules.14"],"modules.47":["sections.1.modules.15"],"modules.48":["sections.1.modules.16"],"modules.49":["sections.1.modules.17"],"modules.50":["sections.1.modules.18"],"modules.51":["sections.1.modules.19"],"modules.52":["sections.1.modules.20"],"modules.53":["sections.1.modules.21"],"modules.54":["sections.1.modules.22"],"modules.55":["sections.1.modules.23"],"modules.56":["sections.1.modules.24"],"modules.57":["sections.1.modules.25"],"modules.58":["sections.1.modules.26"],"modules.59":["sections.1.modules.27"],"modules.60":["sections.1.modules.28"],"modules.61":["sections.1.modules.29"],"modules.62":["sections.1.modules.30"],"modules.63":["sections.1.modules.31"],"modules.64":["sections.1.modules.32"],"modules.65":["sections.2.modules.0"],"modules.66":["sections.2.modules.1"],"modules.67":["sections.2.modules.2"],"modules.68":["sections.2.modules.3"],"modules.69":["sections.2.modules.4"],"modules.70":["sections.2.modules.5"],"modules.71":["sections.2.modules.6"],"modules.72":["sections.2.modules.7"],"modules.73":["sections.2.modules.8"],"modules.74":["sections.2.modules.9"],"modules.75":["sections.2.modules.10"],"modules.76":["sections.2.modules.11"],"modules.77":["sections.2.modules.12"],"modules.78":["sections.2.modules.13"],"modules.79":["sections.2.modules.14"],"modules.80":["sections.2.modules.15"],"modules.81":["sections.2.modules.16"],"modules.82":["sections.2.modules.17"],"modules.83":["sections.2.modules.18"],"modules.84":["sections.2.modules.19"],"modules.85":["sections.2.modules.20"],"modules.86":["sections.2.modules.21"],"modules.87":["sections.2.modules.22"],"modules.88":["sections.2.modules.23"],"modules.89":["sections.2.modules.24"],"modules.90":["sections.2.modules.25"],"modules.91":["sections.2.modules.26"],"modules.92":["sections.2.modules.27"],"modules.93":["sections.2.modules.28"],"modules.94":["sections.2.modules.29"],"modules.95":["sections.2.modules.30"],"modules.96":["sections.2.modules.31"],"modules.97":["sections.2.modules.32"],"modules.98":["sections.2.modules.33"],"modules.99":["sections.2.modules.34"],"modules.100":["sections.2.modules.35"],"modules.101":["sections.2.modules.36"],"modules.102":["sections.2.modules.37"],"modules.103":["sections.2.modules.38"],"modules.104":["sections.2.modules.39"],"modules.105":["sections.2.modules.40"],"modules.106":["sections.2.modules.41"],"modules.107":["sections.2.modules.42"],"modules.108":["sections.2.modules.43"],"modules.109":["sections.2.modules.44"],"modules.110":["sections.2.modules.45"],"modules.111":["sections.2.modules.46"],"modules.112":["sections.2.modules.47"],"modules.113":["sections.2.modules.48"],"modules.114":["sections.2.modules.49"],"modules.115":["sections.2.modules.50"],"modules.116":["sections.2.modules.51"],"modules.117":["sections.2.modules.52"],"modules.118":["sections.2.modules.53"],"modules.119":["sections.2.modules.54"],"modules.120":["sections.2.modules.55"],"modules.121":["sections.2.modules.56"],"modules.122":["sections.2.modules.57"],"modules.123":["sections.2.modules.58"],"modules.124":["sections.2.modules.59"],"modules.125":["sections.2.modules.60"],"modules.126":["sections.2.modules.61"],"modules.127":["sections.2.modules.62"],"modules.128":["sections.2.modules.63"],"modules.129":["sections.2.modules.64"],"modules.130":["sections.2.modules.65"],"modules.131":["sections.2.modules.66"],"modules.132":["sections.3.modules.0"],"modules.133":["sections.3.modules.1"],"modules.134":["sections.3.modules.2"],"modules.135":["sections.3.modules.3"],"modules.136":["sections.3.modules.4"],"modules.137":["sections.3.modules.5"],"modules.138":["sections.3.modules.6"],"modules.139":["sections.3.modules.7"],"modules.140":["sections.3.modules.8"],"modules.141":["sections.3.modules.9"],"modules.142":["sections.3.modules.10"],"modules.143":["sections.3.modules.11"],"modules.144":["sections.3.modules.12"],"modules.145":["sections.3.modules.13"],"modules.146":["sections.3.modules.14"],"modules.147":["sections.4.modules.0"],"modules.148":["sections.4.modules.1"],"modules.149":["sections.4.modules.2"],"modules.150":["sections.4.modules.3"],"modules.151":["sections.4.modules.4"],"modules.152":["sections.4.modules.5"],"modules.153":["sections.4.modules.6"],"modules.154":["sections.4.modules.7"],"modules.155":["sections.4.modules.8"],"modules.156":["sections.4.modules.9"],"modules.157":["sections.4.modules.10"],"modules.158":["sections.4.modules.11"],"modules.159":["sections.4.modules.12"],"modules.160":["sections.4.modules.13"],"modules.161":["sections.4.modules.14"],"modules.162":["sections.4.modules.15"],"modules.163":["sections.4.modules.16"],"modules.164":["sections.4.modules.17"],"modules.165":["sections.5.modules.0"],"modules.166":["sections.5.modules.1"],"modules.167":["sections.5.modules.2"],"modules.168":["sections.5.modules.3"],"modules.169":["sections.5.modules.4"],"modules.170":["sections.5.modules.5"],"modules.171":["sections.5.modules.6"],"modules.172":["sections.5.modules.7"],"modules.173":["sections.5.modules.8"],"modules.174":["sections.5.modules.9"],"modules.175":["sections.5.modules.10"],"modules.176":["sections.5.modules.11"],"modules.177":["sections.5.modules.12"],"modules.178":["sections.5.modules.13"],"modules.179":["sections.5.modules.14"],"modules.180":["sections.5.modules.15"],"modules.181":["sections.5.modules.16"],"modules.182":["sections.5.modules.17"],"modules.183":["sections.5.modules.18"],"modules.184":["sections.5.modules.19"],"modules.185":["sections.5.modules.20"],"modules.186":["sections.5.modules.21"],"modules.187":["sections.5.modules.22"],"modules.188":["sections.5.modules.23"],"modules.189":["sections.6.modules.0"],"modules.190":["sections.6.modules.1"],"modules.191":["sections.6.modules.2"],"modules.192":["sections.6.modules.3"],"modules.193":["sections.6.modules.4"],"modules.194":["sections.6.modules.5"],"modules.195":["sections.6.modules.6"],"modules.196":["sections.6.modules.7"],"modules.197":["sections.6.modules.8"],"modules.198":["sections.6.modules.9"],"modules.199":["sections.6.modules.10"],"modules.200":["sections.6.modules.11"],"modules.201":["sections.6.modules.12"],"modules.202":["sections.6.modules.13"],"modules.203":["sections.6.modules.14"],"modules.204":["sections.6.modules.15"],"modules.205":["sections.6.modules.16"],"modules.206":["sections.6.modules.17"],"modules.207":["sections.6.modules.18"],"modules.208":["sections.6.modules.19"],"modules.209":["sections.6.modules.20"],"modules.210":["sections.6.modules.21"],"modules.211":["sections.6.modules.22"],"modules.212":["sections.6.modules.23"],"modules.213":["sections.7.modules.0"],"modules.214":["sections.7.modules.1"],"modules.215":["sections.7.modules.2"],"modules.216":["sections.7.modules.3"],"modules.217":["sections.7.modules.4"],"modules.218":["sections.8.modules.0"],"modules.219":["sections.8.modules.1"],"modules.220":["sections.8.modules.2"],"modules.221":["sections.8.modules.3"],"modules.222":["sections.8.modules.4"],"modules.223":["sections.8.modules.5"],"modules.224":["sections.8.modules.6"],"modules.225":["sections.8.modules.7"],"modules.226":["sections.8.modules.8"],"modules.227":["sections.8.modules.9"],"modules.228":["sections.8.modules.10"],"modules.229":["sections.8.modules.11"],"modules.230":["sections.8.modules.12"],"modules.231":["sections.8.modules.13"],"modules.232":["sections.8.modules.14"],"modules.233":["sections.8.modules.15"],"modules.234":["sections.8.modules.16"],"modules.235":["sections.8.modules.17"],"modules.236":["sections.9.modules.0"],"modules.237":["sections.9.modules.1"],"modules.238":["sections.9.modules.2"],"modules.239":["sections.9.modules.3"],"modules.240":["sections.9.modules.4"],"modules.241":["sections.9.modules.5"],"modules.242":["sections.9.modules.6"],"modules.243":["sections.10.modules.0"],"modules.244":["sections.10.modules.1"],"modules.245":["sections.10.modules.2"],"modules.246":["sections.10.modules.3"],"modules.247":["sections.10.modules.4"],"modules.248":["sections.10.modules.5"],"modules.249":["sections.10.modules.6"],"modules.250":["sections.10.modules.7"],"modules.251":["sections.10.modules.8"],"modules.252":["sections.10.modules.9"],"modules.253":["sections.10.modules.10"],"modules.254":["sections.10.modules.11"],"modules.255":["sections.10.modules.12"],"modules.256":["sections.10.modules.13"],"modules.257":["sections.10.modules.14"],"modules.258":["sections.10.modules.15"],"modules.259":["sections.10.modules.16"],"modules.260":["sections.10.modules.17"],"modules.261":["sections.10.modules.18"],"modules.262":["sections.10.modules.19"],"modules.263":["sections.10.modules.20"],"modules.264":["sections.10.modules.21"],"modules.265":["sections.10.modules.22"],"modules.266":["sections.10.modules.23"],"modules.267":["sections.10.modules.24"],"modules.268":["sections.10.modules.25"],"modules.269":["sections.10.modules.26"],"modules.270":["sections.10.modules.27"],"modules.271":["sections.10.modules.28"],"modules.272":["sections.11.modules.0"],"modules.273":["sections.11.modules.1"],"modules.274":["sections.11.modules.2"],"modules.275":["sections.11.modules.3"],"modules.276":["sections.11.modules.4"],"modules.277":["sections.11.modules.5"],"modules.278":["sections.11.modules.6"],"modules.279":["sections.11.modules.7"],"modules.280":["sections.11.modules.8"],"modules.281":["sections.11.modules.9"],"modules.282":["sections.11.modules.10"],"modules.283":["sections.11.modules.11"],"modules.284":["sections.11.modules.12"],"modules.285":["sections.11.modules.13"],"modules.286":["sections.11.modules.14"],"modules.287":["sections.11.modules.15"],"modules.288":["sections.11.modules.16"],"modules.289":["sections.11.modules.17"],"modules.290":["sections.11.modules.18"],"modules.291":["sections.11.modules.19"],"modules.292":["sections.11.modules.20"],"modules.293":["sections.11.modules.21"],"modules.294":["sections.11.modules.22"],"modules.295":["sections.11.modules.23"],"modules.296":["sections.11.modules.24"],"modules.297":["sections.11.modules.25"],"modules.298":["sections.11.modules.26"],"modules.299":["sections.11.modules.27"],"modules.300":["sections.11.modules.28"],"modules.301":["sections.11.modules.29"],"modules.302":["sections.11.modules.30"],"modules.303":["sections.11.modules.31"],"modules.304":["sections.11.modules.32"],"modules.305":["sections.11.modules.33"],"modules.306":["sections.11.modules.34"],"modules.307":["sections.11.modules.35"],"modules.308":["sections.11.modules.36"],"modules.309":["sections.11.modules.37"],"modules.310":["sections.11.modules.38"],"modules.311":["sections.11.modules.39"],"modules.312":["sections.11.modules.40"],"modules.313":["sections.11.modules.41"],"modules.314":["sections.11.modules.42"],"modules.315":["sections.11.modules.43"],"modules.316":["sections.11.modules.44"],"modules.317":["sections.11.modules.45"],"modules.318":["sections.11.modules.46"],"modules.319":["sections.11.modules.47"],"modules.320":["sections.11.modules.48"],"modules.321":["sections.11.modules.49"],"modules.322":["sections.11.modules.50"],"modules.323":["sections.11.modules.51"],"modules.324":["sections.11.modules.52"],"modules.325":["sections.11.modules.53"],"modules.326":["sections.11.modules.54"],"modules.327":["sections.11.modules.55"],"modules.328":["sections.11.modules.56"],"modules.329":["sections.11.modules.57"],"modules.330":["sections.11.modules.58"],"modules.331":["sections.12.modules.0"],"modules.332":["sections.12.modules.1"],"modules.333":["sections.12.modules.2"],"modules.334":["sections.12.modules.3"],"modules.335":["sections.12.modules.4"],"modules.336":["sections.12.modules.5"],"modules.337":["sections.12.modules.6"],"modules.338":["sections.13.modules.0"],"modules.339":["sections.13.modules.1"],"modules.340":["sections.13.modules.2"],"modules.341":["sections.13.modules.3"],"modules.342":["sections.13.modules.4"],"modules.343":["sections.13.modules.5"],"modules.344":["sections.14.modules.0"],"modules.345":["sections.14.modules.1"],"modules.346":["sections.14.modules.2"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792296763910,"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-patterns"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getBySlug\"],{\"input\":{\"slug\":\"machine-learning-patterns\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764262,"state":{"data":{"json":{"path":{"archivedAt":null,"id":"seed_7f6b8faa6086f2b6271ead87974bea32","slug":"machine-learning-patterns","title":"Machine Learning Patterns","description":"Master Machine Learning from fundamentals to advanced techniques through 19 comprehensive chapters. This path covers 318 carefully curated problems spanning linear algebra, probability, classical ML, deep learning, reinforcement learning, and more.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Classical ML Algorithms","Neural Networks","Deep Learning","Reinforcement Learning","NLP & Computer Vision","MLOps & Deployment"],"estimatedHours":155.6,"problemCount":317,"visualizerCount":15,"quizCount":0,"learningCount":15,"difficulty":"ADVANCED","idealFor":["ML engineers","Data scientists","AI researchers","Software engineers transitioning to ML"],"prerequisites":["Basic Python programming","Understanding of calculus concepts","Familiarity with linear algebra","Basic statistics knowledge"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithm implementation","Neural network design and training","Deep learning architectures","Reinforcement learning techniques","Model evaluation and optimization"],"outcomes":["Build ML models from scratch","Understand and implement core algorithms","Design neural network architectures","Deploy and monitor ML systems","Apply ML to real-world problems"],"overview":"This comprehensive learning path covers 318 problems across 19 chapters, taking you from mathematical foundations to advanced ML techniques.\n\n**Learning Path Structure:**\n• Chapters 1-2: Mathematical Foundations - Linear algebra and probability\n• Chapter 3: Data Preprocessing - Feature engineering and data pipelines\n• Chapter 4: Calculus & Optimization - Gradients and optimization theory\n• Chapter 5-6: Classical ML - Traditional algorithms and evaluation metrics\n• Chapters 7-8: Neural Networks - Fundamentals and training techniques\n• Chapters 9-11: Deep Learning - CNNs, RNNs, and Transformers\n• Chapters 12-14: Advanced Topics - Modern architectures and RL\n• Chapters 15-18: Applications - NLP, CV, LLM evaluation, and MLOps\n\nEach chapter builds on previous concepts for a cohesive learning experience.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.217Z","modules":[{"id":"seed_0fed999e26d2c1b303da9c0446066784","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_c8f5199eee51f755e2c074360082ace1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Coordinate Transformation Between Bases","order":2,"contentType":"VISUALIZER","contentSlug":"basis-change-matrix","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_97b73c7659a4b536f5744fbb0b4bfc04","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Coordinate Transformation Between Bases","order":3,"contentType":"PROBLEM","contentSlug":"basis-change-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_903c973d066bfea48b4a823ed2cac448","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Closed-Form Linear Model Fitting","order":4,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b6dab6ca30f6f61ab92fb8bf0e4d491f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Column Compressed Sparse Matrix Format","order":5,"contentType":"PROBLEM","contentSlug":"column-compressed-sparse-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5632f9e5f34a9561ec354d1e40beb96d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Component-wise Vector Addition","order":6,"contentType":"PROBLEM","contentSlug":"component-wise-vector-addition","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6f6e1e827e3cb3af8fe6fd6ccefa0b4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Linear Kernel Computation (Dot Product Similarity)","order":7,"contentType":"PROBLEM","contentSlug":"linear-kernel-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1598b2cf3105e4f03843157652ddce4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Linear Transformation of a Vector","order":8,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3565d8e495f25484809081d148299e41","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Square Matrix Determinant and Trace Evaluator","order":9,"contentType":"PROBLEM","contentSlug":"matrix-determinant-trace-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_cbc8bb101e85ad2a916dfec6da7b6e87","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Matrix Dimension Restructuring","order":10,"contentType":"PROBLEM","contentSlug":"matrix-dimension-restructuring","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1c2b95703f1f152f88770b9594885bb0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":11,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_207e2cec4be1ca1c571596755c670529","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Matrix Row-Column Interchange","order":12,"contentType":"PROBLEM","contentSlug":"matrix-row-column-interchange","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f7091d31b102288e0a69f2cf94268c5b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Scaling a Matrix by a Constant Factor","order":13,"contentType":"PROBLEM","contentSlug":"matrix-scalar-multiplication","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_bb59409c386fe7e029eaf9301363f133","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Polynomial Kernel Similarity Computation","order":14,"contentType":"PROBLEM","contentSlug":"polynomial-kernel-similarity","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d11433d22c48db97f8ffe65350b79e39","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Hyperplane Separation Width for Linear Classifiers","order":15,"contentType":"PROBLEM","contentSlug":"svm-margin-width-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_aa89700510b7518658b7d16193681876","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Three-Dimensional Vector Cross Product","order":16,"contentType":"PROBLEM","contentSlug":"three-dimensional-vector-cross-product","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_faeba8da6151a849971ece0998257c32","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vector Inner Product Computation","order":17,"contentType":"PROBLEM","contentSlug":"vector-inner-product","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c4232feb07cbfcbc6e65b49b2183809d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Vector Projection onto a Line","order":18,"contentType":"PROBLEM","contentSlug":"vector-projection-onto-line","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_bad64b9af305e248c2be8416530eb0b5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Construct Diagonal Matrix from Vector","order":19,"contentType":"PROBLEM","contentSlug":"vector-to-diagonal-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a95962ea6c455750ed761d28b4ee7e85","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Column Space Basis Extraction","order":20,"contentType":"PROBLEM","contentSlug":"column-space-basis-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_9e6f03f6c505e038040fe4df423d5c1f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Doubly Stochastic Stream Mixing Forward Pass","order":21,"contentType":"PROBLEM","contentSlug":"doubly-stochastic-stream-mixing","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0bc241e4fdd8c27be93ab6cccf132c5f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Gauss-Seidel Linear System Solver","order":22,"contentType":"PROBLEM","contentSlug":"gauss-seidel-linear-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6c9e2c1e97f2105c250c6860f6a0f054","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Gaussian Kernel Similarity Matrix","order":23,"contentType":"PROBLEM","contentSlug":"gaussian-kernel-similarity-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a6182437e9cfb27f65436eee2ed3ecfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Hyperdimensional Row Encoding Using Bind-Bundle Operations","order":24,"contentType":"PROBLEM","contentSlug":"hyperdimensional-row-encoding","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5c32b1938f6387a424492f58adfb4eab","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Low-Rank Adaptation Forward Computation","order":25,"contentType":"PROBLEM","contentSlug":"low-rank-adaptation-forward","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5856e5818a47891b9a861f34ca8cdb10","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Pairwise Matrix Product Computation","order":26,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_52ca1368702630a521c670495fc4fa95","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Surprise-Driven Neural Memory Update with Momentum","order":27,"contentType":"PROBLEM","contentSlug":"neural-memory-update-surprise-momentum","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a5b9c715441cca71a6f409f381c244fe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Two-Dimensional Orthonormal Basis Generator","order":28,"contentType":"PROBLEM","contentSlug":"orthonormal-basis-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_109d41d107af77032f4728124d1ef433","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Row Reduced Canonical Form","order":29,"contentType":"PROBLEM","contentSlug":"row-reduced-canonical-form","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8a84840694f53c72fe7d4aa1e969e27f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Similarity Matrix Transformation","order":30,"contentType":"PROBLEM","contentSlug":"similarity-matrix-transform","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f0ad4092675213ad0e865668e412989d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Spatial Translation Transformation in 2D","order":31,"contentType":"PROBLEM","contentSlug":"spatial-translation-transform","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_46f7916ce54b82dd2066c09bca95f228","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Linear algebra","sectionOrder":1,"title":"Spectral Analysis of a Square Matrix","order":32,"contentType":"PROBLEM","contentSlug":"spectral-analysis-square-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e4e297ce0a6812999d072c08747f51a6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_bc8045d3a49673a818445952dfe6b770","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Binary Association Coefficient","order":2,"contentType":"VISUALIZER","contentSlug":"binary-association-coefficient","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_99eeb4f46f0af3e65915450c1b6c3f99","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Binary Association Coefficient","order":3,"contentType":"PROBLEM","contentSlug":"binary-association-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_42915cae18a625eda2d851cad7f13d93","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Dataset Summary Metrics Calculator","order":4,"contentType":"PROBLEM","contentSlug":"dataset-summary-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_481443a6ae37d1c0add7d75d9adad58b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Discrete Frequency Distribution Calculator","order":5,"contentType":"PROBLEM","contentSlug":"discrete-frequency-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f8955cfe865f6f4a0e61f03d43e83d71","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Statistical Moments of a Uniform Die","order":6,"contentType":"PROBLEM","contentSlug":"fair-dice-statistics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e2f5cf634181f1ad5fcd49af54ba7d01","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Information Divergence Between Gaussian Distributions","order":7,"contentType":"PROBLEM","contentSlug":"gaussian-distribution-divergence","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_92c06a1016cca53b87c18f94459bf4b7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Nonlinear Activator","order":8,"contentType":"PROBLEM","contentSlug":"gaussian-nonlinear-activator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6d27d96e55634041fc446917b831c109","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Log Softmax Transformation","order":9,"contentType":"PROBLEM","contentSlug":"log-softmax-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_261f2a0afd9c2ef268045872640a3fdc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Compute Arithmetic Mean Along Matrix Axis","order":10,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e2858d50c8fceacc268a12f2d7a8c5ae","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Computing Event Likelihood Using the Poisson Model","order":11,"contentType":"PROBLEM","contentSlug":"poisson-event-likelihood","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6a4428aaedd87a88cf88792fbfe54c88","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Sample Mean Distribution Simulator","order":12,"contentType":"PROBLEM","contentSlug":"sample-mean-distribution-simulator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_482a188ea8f13767632a86d9372cce41","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Logistic Sigmoid Transformation","order":13,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5a30384cf824beed66ad8701dd0c3bba","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Softmax Probability Distribution","order":14,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c6507663b48270175326bb689e6b81b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Aggregate Probability Computation via Event Partitioning","order":15,"contentType":"PROBLEM","contentSlug":"aggregate-probability-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_55ad8c56b5b3410a66f3295264611dea","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Bernoulli Trial Success Probability","order":16,"contentType":"PROBLEM","contentSlug":"bernoulli-trial-success-probability","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7bad12b008e115f4e4485c7b44e9720f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Chi-Square Distribution Density Evaluator","order":17,"contentType":"PROBLEM","contentSlug":"chi-square-density-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_baa6532bd31f7e84f893b766f9cf15f5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Conditional Likelihood from Joint Event Distribution","order":18,"contentType":"PROBLEM","contentSlug":"conditional-likelihood-from-joint-events","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b274badce3ca232189203f8ec1a640a2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Pearson Correlation Matrix Computation","order":19,"contentType":"PROBLEM","contentSlug":"correlation-matrix-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f1833af17e7ec7d2d543693fa0f515ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Experiment Detection Power Analysis","order":20,"contentType":"PROBLEM","contentSlug":"experiment-detection-power-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_067155b8e858b14423f3a11e7801901d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Finite Sampling Probability Calculator","order":21,"contentType":"PROBLEM","contentSlug":"finite-sampling-probability-calculator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_873a83ec1931f2bdcbd4816612aa9d46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Distribution Divergence Measure","order":22,"contentType":"PROBLEM","contentSlug":"gaussian-distribution-divergence-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fae9aa499ff4a92874361b2616613508","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Gaussian Probability Density Evaluator","order":23,"contentType":"PROBLEM","contentSlug":"gaussian-probability-density-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3a632d989564f877884bf768d057807a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Independent Samples Mean Comparison","order":24,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_81eb624d8db2a19e99945991e36b5846","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Shared Information from Joint Probability Distribution","order":25,"contentType":"PROBLEM","contentSlug":"joint-distribution-information-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_de4d99ac8f4792f78b7cb6060ff166cd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Computing Covariance from Joint Probability Distributions","order":26,"contentType":"PROBLEM","contentSlug":"joint-pmf-covariance-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_da1b205eeb3f533f39d298a47dc9c8d9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Failure Count Probability Before Success Goal","order":27,"contentType":"PROBLEM","contentSlug":"negative-binomial-failure-probability","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_82fcbfe1a66f542a425ae5e00c65679f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Principal Component Extraction","order":28,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_087a261a23ebffa5552dadb354e7be4e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Shared Date Coincidence Calculator","order":29,"contentType":"PROBLEM","contentSlug":"shared-date-coincidence-calculator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_887a831ac0eb178d9d25ff229253538d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Probability-Weighted Ensemble Classification","order":30,"contentType":"PROBLEM","contentSlug":"soft-voting-ensemble-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7dcdc25128a926e29296ee7c94997b7c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Stochastic State Evolution Simulator","order":31,"contentType":"PROBLEM","contentSlug":"stochastic-state-evolution-simulator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_eec23075bb59f73087781c694f02fa78","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Symmetric Distribution Divergence Measure","order":32,"contentType":"PROBLEM","contentSlug":"symmetric-distribution-divergence-measure","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2df4bfe26b8990979ce54b5ad5a4104c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Probability and statistics","sectionOrder":2,"title":"Bayesian Function Predictor","order":33,"contentType":"PROBLEM","contentSlug":"bayesian-function-predictor","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_dbf5e5a1b2c29e35c52e2bda6e309d7f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_0100abc09d0448605acf6efc210cc3b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Activation Function Gradient Computation","order":2,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6445aca82b4c6c784112cd82d058cd08","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Activation Function Gradient Computation","order":3,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ca90fbb7a6603e75f7e251f035e96afb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adamax Gradient Optimizer Update Step","order":4,"contentType":"PROBLEM","contentSlug":"adamax-gradient-optimizer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e694ebf6feab61a29e05d7f015263128","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Gradient Parameter Updater","order":5,"contentType":"PROBLEM","contentSlug":"adaptive-gradient-parameter-updater","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f8ac39dcb399df89ea1573b73b270906","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Binary Classification Loss","order":6,"contentType":"PROBLEM","contentSlug":"binary-classification-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_991f8ffc82bcc2922c28b17b561c32e6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Bounded Gradient Constraint for Training Stabilization","order":7,"contentType":"PROBLEM","contentSlug":"bounded-gradient-constraint","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3c0bd0bb8e486fcb65a908c925cd8a19","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sparse Matrix Compression: Row-Based Storage Transformation","order":8,"contentType":"PROBLEM","contentSlug":"csr-sparse-matrix-conversion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_eee0ed04779c005c65c278cd5834e426","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Epoch-Based Learning Rate Decay Scheduler","order":9,"contentType":"PROBLEM","contentSlug":"epoch-based-learning-rate-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f7ba97adcb9ac42115512f9cb8c9eb24","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Exponential Decay Learning Rate Scheduler","order":10,"contentType":"PROBLEM","contentSlug":"exponential-decay-learning-rate","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_765811a293443f5276de0c162c60f4fa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":11,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_be6b57dd476ff3803fbc4179e89098c4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Vector Analysis for Optimization","order":12,"contentType":"PROBLEM","contentSlug":"gradient-vector-analysis","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_cf1085ea55d62ff1c7b8e65b2ed17110","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Descent with L2 Regularization Penalty","order":13,"contentType":"PROBLEM","contentSlug":"l2-regularization-weight-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2f78e0cb7b8c967b23a4c6340b985469","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Margin Penalty Loss Computation for Binary Classifiers","order":14,"contentType":"PROBLEM","contentSlug":"margin-penalty-loss-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e99b7b03bc660cae78cc3c7448fef5b7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Momentum-Based Parameter Updater","order":15,"contentType":"PROBLEM","contentSlug":"momentum-based-parameter-updater","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a4c6878e675339d6123fc6cbe0dda305","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Nesterov Accelerated Momentum Optimizer","order":16,"contentType":"PROBLEM","contentSlug":"nesterov-momentum-optimizer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0540bc4ca1a7b3f0c5b0dc7b7e143cad","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Piecewise Linear Sigmoid Approximation","order":17,"contentType":"PROBLEM","contentSlug":"piecewise-linear-sigmoid-approximation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4c5cc0b61a6b01e7b4f64d4bc1cc6274","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Series Function Estimation","order":18,"contentType":"PROBLEM","contentSlug":"polynomial-series-function-estimation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_14ae101cb19b475926980112999bca78","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Term Rate of Change","order":19,"contentType":"PROBLEM","contentSlug":"polynomial-term-derivative","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fe87c5037abd4d567d45e6d275edef09","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sequential Function Chain Evaluator","order":20,"contentType":"PROBLEM","contentSlug":"sequential-function-chain-evaluator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f2753046c950e86a85fa65b38c0a50f3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training Convergence Halt Detector","order":21,"contentType":"PROBLEM","contentSlug":"training-convergence-halt-detector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_83eec4b77169e413ac846a2084df5f81","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training Halt Monitor: Validation Stagnation Detector","order":22,"contentType":"PROBLEM","contentSlug":"training-halt-monitor","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e5adc58490f27c31475a789d6d3b2caf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Delta Gradient Optimizer","order":23,"contentType":"PROBLEM","contentSlug":"adaptive-delta-gradient-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_54f835afd6abc355051c6b275ff98698","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Gradient Scaling Optimizer","order":24,"contentType":"PROBLEM","contentSlug":"adaptive-gradient-scaling-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5fd25ee096bf2f1be0b29cca6f4ab810","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Moment Estimation Optimizer Step","order":25,"contentType":"PROBLEM","contentSlug":"adaptive-moment-estimation-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_afe9920decead522af47917f2870be60","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Adaptive Moment Estimation Optimizer","order":26,"contentType":"PROBLEM","contentSlug":"adaptive-moment-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a8149de2ae9f67239f84c42e7a5ea215","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Segmented 8-Bit Floating Point Tensor Compression","order":27,"contentType":"PROBLEM","contentSlug":"blockwise-fp8-quantization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_031e7d71c2c9cbab94be812cba78de07","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Composite Function Differentiation via Chain Rule","order":28,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1d70ad9216f470dffc457cc6d43d0f8f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Constrained Quadratic Optimizer with Dual Variables","order":29,"contentType":"PROBLEM","contentSlug":"constrained-quadratic-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c878243e2f770ed9c6307374b7904496","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Cosine Decay Learning Rate Scheduler","order":30,"contentType":"PROBLEM","contentSlug":"cosine-annealing-learning-rate-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_38944d0a6de3f4668782e5292ad025aa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Curvature-Based Optimization Step","order":31,"contentType":"PROBLEM","contentSlug":"curvature-based-optimization-step","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c940806ae1635c5228535651eb66f1a0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Decoupled Weight Decay Parameter Update","order":32,"contentType":"PROBLEM","contentSlug":"decoupled-weight-decay-parameter-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_851e52c008dd224a995132fde05ec2db","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Determinant-Based Linear System Solver","order":33,"contentType":"PROBLEM","contentSlug":"determinant-based-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4e5979faf45510a1b37ef933b81d44bd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Exhaustive Hyperparameter Search","order":34,"contentType":"PROBLEM","contentSlug":"exhaustive-hyperparameter-search","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_97b4c83f1970fd3799453c99222712e7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Direct Linear System Solver Using Row Reduction","order":35,"contentType":"PROBLEM","contentSlug":"gaussian-elimination-linear-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a1aaadaa75eef9954e0be670780bd9a9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Global Norm Gradient Rescaling","order":36,"contentType":"PROBLEM","contentSlug":"global-norm-gradient-rescaling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3b09a3b663e1b262860de1ece518d31b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Glorot/Xavier Weight Initialization","order":37,"contentType":"PROBLEM","contentSlug":"glorot-weight-initialization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f0284703f3c55a1b107ed0b3b4acacfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Field Matrix Computation","order":38,"contentType":"PROBLEM","contentSlug":"gradient-field-matrix-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5593f671425ea59616856d47e1664cd0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Vector Computation for Scalar Fields","order":39,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c0c5b22fe6852b5e5223d47a4c128743","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient Verification via Finite Difference Approximation","order":40,"contentType":"PROBLEM","contentSlug":"gradient-verification-finite-difference","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_aae5bf36a7ab80d47f70e2a390e82f46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hessian-Based Critical Point Classification","order":41,"contentType":"PROBLEM","contentSlug":"hessian-critical-point-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_810e4f3b9c43539eb94eaf8603d474c6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hybrid Precision Computation for Deep Learning","order":42,"contentType":"PROBLEM","contentSlug":"hybrid-precision-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2b19216f6da8e6ed2c50a9548b43e60b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Hybrid Regularized Linear Optimizer","order":43,"contentType":"PROBLEM","contentSlug":"hybrid-regularized-linear-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7524eebcf106ebef5b2ac0efa2516f0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Iterative Linear System Solver","order":44,"contentType":"PROBLEM","contentSlug":"iterative-linear-system-solver","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_51b3da489a4f97a6463ef65c3fcbc251","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Momentum-Based Orthogonal Optimizer with Iterative Matrix Conditioning","order":45,"contentType":"PROBLEM","contentSlug":"muon-optimizer-newton-schulz-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ad754ffaa9e27b2907c6ae66d797e950","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Muon Optimizer Update with Newton-Schulz Preconditioning","order":46,"contentType":"PROBLEM","contentSlug":"muon-optimizer-step","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f9fd8d72df8d22b797c04cccd4b03353","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Parameter Optimization Using Gradient-Based Algorithms","order":47,"contentType":"PROBLEM","contentSlug":"optimization-step-algorithms","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0654867f33edc12c165d8e0d136f97ef","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Polynomial Global Minimum Finder","order":48,"contentType":"PROBLEM","contentSlug":"polynomial-global-minimum-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1afd7f6594073b2cb1635a2ffebe704a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Derivative of Polynomial Ratio Using Division Rule","order":49,"contentType":"PROBLEM","contentSlug":"polynomial-quotient-derivative","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_42fc70bfbeb049dbf9f607647ce1aecd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Quantized Low-Rank Adaptation Forward Pass","order":50,"contentType":"PROBLEM","contentSlug":"quantized-lowrank-adaptation-forward","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_22217ca3bcec23d1fe47134e588781b6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Robust Hybrid Regression Loss","order":51,"contentType":"PROBLEM","contentSlug":"robust-hybrid-regression-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2a8692ae0dd2ee91b0fa92a053608bf2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Second-Order Curvature Matrix Computation","order":52,"contentType":"PROBLEM","contentSlug":"second-order-curvature-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8d89700158fb5363c6fb0422ac14c330","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Masked Gradient Descent for Localized Knowledge Editing","order":53,"contentType":"PROBLEM","contentSlug":"selective-gradient-masking-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_21bfc8b82c137747c6d80787d42f17d2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Training a Single Neuron with Gradient Descent","order":54,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b009e73490398c8df40dcf690a773d79","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Gradient of Softmax Cross-Entropy Loss","order":55,"contentType":"PROBLEM","contentSlug":"softmax-cross-entropy-gradient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_18ce8af11e0618babea3c8b86911a027","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Jacobian Matrix of the Softmax Transformation","order":56,"contentType":"PROBLEM","contentSlug":"softmax-jacobian-matrix","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c2355f8cc7fa20fbf9f3c572371e2a2b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Sparse Regression Optimizer with L1 Penalty","order":57,"contentType":"PROBLEM","contentSlug":"sparse-regression-optimizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5d03e076a949d371eec2b7722538e7e3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Dynamic Temperature Scheduling for Stochastic Neural Networks","order":58,"contentType":"PROBLEM","contentSlug":"temperature-decay-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5023651c6676e627a90ba7d195b58d44","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Warmup Cosine Annealing Learning Rate Scheduler","order":59,"contentType":"PROBLEM","contentSlug":"warmup-cosine-annealing-scheduler","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0d437d616850321420fa0c57d5b97753","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Binary Classifier Gradient Optimizer","order":60,"contentType":"PROBLEM","contentSlug":"binary-classifier-gradient-optimizer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f4333d7d25b22a60330042f946838d44","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Iterative Solution of Symmetric Positive-Definite Linear Systems Using Conjugate Gradients","order":61,"contentType":"PROBLEM","contentSlug":"cg-linear-system-solver","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e1203a84133f433d3f68b92ae2332e2c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Computing the Determinant of a 4×4 Matrix via Cofactor Expansion","order":62,"contentType":"PROBLEM","contentSlug":"determinant-4x4-laplace-expansion","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b3943d9a73df36daca86433b2c3f71a4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Two-by-Two Matrix Decomposition via Jacobi Rotation","order":63,"contentType":"PROBLEM","contentSlug":"jacobi-singular-value-decomposition","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_509e0bba90db9d87b3b5562d5bbe2d8c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Kernel-Based Support Vector Classifier Training","order":64,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0280ec8acc24668cb80fe79098df5c53","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Orthogonal-Triangular Matrix Factorization","order":65,"contentType":"PROBLEM","contentSlug":"orthogonal-triangular-factorization","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_525aa14ed693200b9b27d7361691dbc3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Decomposing a 2×2 Matrix via Singular Value Factorization","order":66,"contentType":"PROBLEM","contentSlug":"singular-value-decomposition-2x2","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_955dba231e8d596d43cc5331020e0978","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Calculus and optimization","sectionOrder":3,"title":"Softmax Classifier Optimization via Gradient Descent","order":67,"contentType":"PROBLEM","contentSlug":"softmax-classifier-optimization","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_66cf640e44c4fe692c4b5647b652e995","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_a95179e6f90ec1d2735e5938f915643a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Binary Encoding","order":2,"contentType":"VISUALIZER","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8d495474fc606fdb2fd0a0fa3b1a2d60","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Binary Encoding","order":3,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_10cea7ba6ccc217ea27fd05609a1f7b9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Categorical Distribution Entropy","order":4,"contentType":"PROBLEM","contentSlug":"categorical-distribution-entropy","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_681ef49b03a8c2d3406181cce3bcd37c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Dataset Batch Generator for Training Pipelines","order":5,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e31d188fd627a103fd3798bca5161a92","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Feature Covariance Computation","order":6,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ab66e556b62230c65f315fe38843d27b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":7,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6e62fe0b6131035bbd392c1383a033f5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Interval Normalization Transform","order":8,"contentType":"PROBLEM","contentSlug":"interval-normalization-transform","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4b797764424d7da343050834482e9a59","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Polynomial Basis Expansion for Feature Engineering","order":9,"contentType":"PROBLEM","contentSlug":"polynomial-basis-expansion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_351dac0d10dbcb5dce1f2d21996e5d7d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Range-Bounded Feature Scaling","order":10,"contentType":"PROBLEM","contentSlug":"range-bounded-feature-scaling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0d62d7870facaa1c45b65ed1489b49ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Synchronized Data Permutation","order":11,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2d73f65475af3cd309db22bcd92ea617","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Ordered Polynomial Feature Generator","order":12,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7df2eaa815f1ec39988e72a494ba5ad0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Partition Dataset by Feature Threshold","order":13,"contentType":"PROBLEM","contentSlug":"partition-data-by-threshold","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_53c658f4b182b34f946cb96c13a3b347","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Proportional Class Partitioning for Dataset Splitting","order":14,"contentType":"PROBLEM","contentSlug":"proportional-class-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d9e14b161ee8138ac0860422461259da","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Feature preparation","sectionOrder":4,"title":"Random Dataset Subset Generation for Ensemble Learning","order":15,"contentType":"PROBLEM","contentSlug":"random-dataset-subsets","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_72021d593fe624b8eebf1d57d65abc0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_a0977e62147804e59e048f5d910941c9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Categorical Cross-Entropy Loss Computation","order":2,"contentType":"VISUALIZER","contentSlug":"categorical-cross-entropy-loss","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_dc1be4093a2ecfc83d90373fbcab0e86","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Categorical Cross-Entropy Loss Computation","order":3,"contentType":"PROBLEM","contentSlug":"categorical-cross-entropy-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d5975b6bbe0ff570428e162b16888d45","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Majority Vote Ensemble Classifier","order":4,"contentType":"PROBLEM","contentSlug":"majority-vote-ensemble-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4f7d8b7b0d3629f717d222b068b0ce62","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"L2-Regularized Linear Regression Loss Computation","order":5,"contentType":"PROBLEM","contentSlug":"regularized-linear-regression-loss","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_207742d11d88842644f10865e713a36a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Sigmoid-Based Binary Classifier","order":6,"contentType":"PROBLEM","contentSlug":"sigmoid-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_535b94f4cf2b06f9f8eeaf23ba6237fe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":7,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_59c9218455f2aeea9d3f3979501bb1ed","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_22b2a135e9cbaa4897364db60fba644e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Decision Tree Complexity Threshold Analysis","order":9,"contentType":"PROBLEM","contentSlug":"decision-tree-complexity-threshold-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_46e66455ec384966b4298b5ded636fc4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Density-Based Spatial Clustering","order":10,"contentType":"PROBLEM","contentSlug":"density-based-spatial-clustering","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_16e5b8e81ff39d4a96dbd645a3bb9765","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Energy Demand Forecasting with Seasonal Detrending","order":11,"contentType":"PROBLEM","contentSlug":"energy-demand-forecasting-with-detrending","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_dd59ca1b70a461bf9a7a042dc22efb5e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Focused Loss Computation for Class Imbalance","order":12,"contentType":"PROBLEM","contentSlug":"focused-loss-imbalanced-classification","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_543709c06a2f6bb1b6ed3d0659086877","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Information Gain Split Finder","order":13,"contentType":"PROBLEM","contentSlug":"information-gain-split-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_766922035a5ee2a9d88cbed8892aaf58","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Optimal Gini Split Finder for Binary Classification Trees","order":14,"contentType":"PROBLEM","contentSlug":"optimal-gini-split-finder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e07ea8bb8ce6508523c05dc51d9c2965","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Regression Tree Predictor","order":15,"contentType":"PROBLEM","contentSlug":"regression-tree-predictor","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3e9df52e7744d108d736d0aa82fed79e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Soft Target Classification Loss","order":16,"contentType":"PROBLEM","contentSlug":"soft-target-classification-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a03ceb34b458f144843233ffba16ccaa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"AdaBoost Ensemble Classifier Training","order":17,"contentType":"PROBLEM","contentSlug":"adaboost-ensemble-training","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_165d8c0eb6ca80ef55ce47b9e6578c26","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Classical machine learning","sectionOrder":5,"title":"Recursive Classification Tree Builder","order":18,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fea4e09050bb21ca355f2100df5d88e9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_c44fafe12687fdd81ae40c4c76200295","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Adaptive Hyperbolic Tangent Transformation","order":2,"contentType":"VISUALIZER","contentSlug":"adaptive-hyperbolic-tangent-transformation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5a51e7cab867b9be59e87ef9999df60e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Adaptive Hyperbolic Tangent Transformation","order":3,"contentType":"PROBLEM","contentSlug":"adaptive-hyperbolic-tangent-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_83e0d18d3f6f9d32f9bc37574664b49d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Bounded Linear Activation Function","order":4,"contentType":"PROBLEM","contentSlug":"bounded-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b07e2fae5804803dbc9daedc5efc8bca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Exponential Linear Activation Function","order":5,"contentType":"PROBLEM","contentSlug":"exponential-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_115b6a51aa3c3f6b830573df7709abfb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Hyperbolic Tangent Activation Function","order":6,"contentType":"PROBLEM","contentSlug":"hyperbolic-tangent-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_92649f6b13a26e8a0d399d3baee1a6c1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Kaiming Weight Initialization Strategy","order":7,"contentType":"PROBLEM","contentSlug":"kaiming-weight-setup","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e837e7c97ce855f2430fb58705cddcc1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Gating Smooth Activation: The Mish Function","order":8,"contentType":"PROBLEM","contentSlug":"mish-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fe84f813221cfd30f6eb60f0965b5e4c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Parametric Rectified Linear Activation","order":9,"contentType":"PROBLEM","contentSlug":"parametric-rectified-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f6c433fc8c08d839cd55fb7702d78eb7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Rectified Linear Activation Function","order":10,"contentType":"PROBLEM","contentSlug":"rectified-linear-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ad9707d65b31922c79380e64bb70ba24","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Gated Neural Activator","order":11,"contentType":"PROBLEM","contentSlug":"self-gated-neural-activator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e92de11877baf74953e005e522c32d0d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Self-Normalizing Exponential Activation","order":12,"contentType":"PROBLEM","contentSlug":"self-normalizing-exponential-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_38fe9ea38f8141e3c53cc30d0d479531","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Skip Connection Residual Unit","order":13,"contentType":"PROBLEM","contentSlug":"skip-connection-residual-unit","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_6e926d6c6a84d594dd89d2ea8c8d17ac","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Smooth Rectifier Activation Function","order":14,"contentType":"PROBLEM","contentSlug":"smooth-rectifier-activation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fe2c44936ce7f758794a0878079c99f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Bounded Smooth Activation: The Softsign Function","order":15,"contentType":"PROBLEM","contentSlug":"softsign-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_202207e82d633377a5a82ee3e4046e94","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Sparse Expert Efficiency Ratio","order":16,"contentType":"PROBLEM","contentSlug":"sparse-expert-efficiency-ratio","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0fd30ecaa9b1ff0741d18922b6016edc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Activation Substitution for Neural Circuit Analysis","order":17,"contentType":"PROBLEM","contentSlug":"activation-substitution-circuit-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f512bcfb0fabdfd58069dd100cd88b20","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Gradient Computation Engine (Basic Autograd)","order":18,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b5dd2777774ef15a1a23a5a1d7a27e11","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Teacher-Student Soft Target Transfer Loss","order":19,"contentType":"PROBLEM","contentSlug":"knowledge-distillation-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_60658fa2562fb8ad1ad5c143a9a066c0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Neural Network Trainable Parameter Counter","order":20,"contentType":"PROBLEM","contentSlug":"neural-network-parameter-counter","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c72ba3e07536ddde11f277da2ba50feb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"RLHF Pre-training Regularization Loss for Knowledge Retention","order":21,"contentType":"PROBLEM","contentSlug":"rlhf-pretraining-regularization-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_51d427355434ba6c870e513998e26344","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Stochastic Neuron Deactivation for Network Regularization","order":22,"contentType":"PROBLEM","contentSlug":"stochastic-neuron-deactivation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_63dca2a5669d7bd6ec9f28d572d1c4af","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Stochastic Top-K Expert Routing for Mixture-of-Experts Networks","order":23,"contentType":"PROBLEM","contentSlug":"stochastic-topk-expert-routing","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2a88be68b95638b537b405fa7f37c40b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Neural networks and efficient training","sectionOrder":6,"title":"Building a Fully Connected Neural Network Layer","order":24,"contentType":"PROBLEM","contentSlug":"fully-connected-layer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_63487fa407d9151beddb3858b22d8ce3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_c2fa590a27b36d87f3228ad6ab06f39c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-wise Batch Normalization for 4D Feature Maps","order":2,"contentType":"VISUALIZER","contentSlug":"batch-normalization-feature-maps","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_10079aaf1b02beab73f273c232c9b590","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-wise Batch Normalization for 4D Feature Maps","order":3,"contentType":"PROBLEM","contentSlug":"batch-normalization-feature-maps","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a09a68891c8d023732e2fe0212e138f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Grayscale Image Luminance Analyzer","order":4,"contentType":"PROBLEM","contentSlug":"grayscale-image-luminance-analyzer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f47b94dc143e3e5843d71998f24cced4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Grayscale Image Intensity Range Analyzer","order":5,"contentType":"PROBLEM","contentSlug":"grayscale-intensity-range-analyzer","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e73e38a83199324361ca7707cf72f343","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Image Border Padding with Zeros","order":6,"contentType":"PROBLEM","contentSlug":"image-border-padding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e801096905ec74483f2a795555266652","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Mirror Image Transformation","order":7,"contentType":"PROBLEM","contentSlug":"mirror-image-transformation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7323baaa7ee35d2ff17c20adf0f02c22","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Perceptual Grayscale Conversion Using Luminosity Weights","order":8,"contentType":"PROBLEM","contentSlug":"rgb-grayscale-luminosity-conversion","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_cde9f59eff08a3b365eabecc22f81ea1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Spatial Channel Aggregation","order":9,"contentType":"PROBLEM","contentSlug":"spatial-channel-aggregation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4dd0ac1e37f1042a8f65373c3c628c4d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Spatial Mean Downsampling","order":10,"contentType":"PROBLEM","contentSlug":"spatial-mean-downsampling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3d43ab3d8483eb0902cd8c3f689ff5f0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Smooth Image Rescaling with Bilinear Interpolation","order":11,"contentType":"PROBLEM","contentSlug":"bilinear-image-scaling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_faf894d2be1d3dc9f86b202091f454b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel Group Normalization","order":12,"contentType":"PROBLEM","contentSlug":"channel-group-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_414c59892f922a40a42059a758530f95","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Channel-Wise Spatial Normalization","order":13,"contentType":"PROBLEM","contentSlug":"channel-wise-spatial-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_285d7bb02cc7da571b76166e9ecbf2d9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"2D Convolution Operation","order":14,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_70848c79ff84428173ff16ef6bd148fb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Cross-Channel Lateral Inhibition Normalization","order":15,"contentType":"PROBLEM","contentSlug":"cross-channel-lateral-inhibition-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0d1414e65eebc633cf420299c30ac116","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Gradient-Based Edge Detection","order":16,"contentType":"PROBLEM","contentSlug":"gradient-based-edge-detection","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d2b882a92457ba62796ded1b60c08818","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Motion Vector Endpoint Error Metric","order":17,"contentType":"PROBLEM","contentSlug":"motion-vector-endpoint-error","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1179f33606b1868c6090bda1889a57e3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Derivative of Polynomial Product Using Product Rule","order":18,"contentType":"PROBLEM","contentSlug":"polynomial-product-derivative","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8e097c0deac496b6dd53b4d86b1c3cca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Strided Maximum Pooling with Overlap","order":19,"contentType":"PROBLEM","contentSlug":"strided-maximum-pooling","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fb519a3a84940622c9cb976b78a92b70","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Bounding Box Overlap Filtering","order":20,"contentType":"PROBLEM","contentSlug":"bounding-box-overlap-filtering","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8bd981e9938c70b1149fe77a73d46865","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Convolutional Network Backpropagation Trainer","order":21,"contentType":"PROBLEM","contentSlug":"convolutional-network-backpropagation-trainer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_4a79d013931cdf445e6725dd5bbb6e26","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Densely Connected Convolutional Block","order":22,"contentType":"PROBLEM","contentSlug":"densely-connected-convolutional-block","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2d8ab26f28f5425c26238859baa4d3cf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"PCA-Based Color Perturbation for Image Augmentation","order":23,"contentType":"PROBLEM","contentSlug":"pca-color-perturbation","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f85c4e762d96fa80631281a637fcde0b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Convolution and computer vision","sectionOrder":7,"title":"Volumetric Convolution Forward Pass","order":24,"contentType":"PROBLEM","contentSlug":"volumetric-convolution-forward","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_be57b7bc82c29511189fafe0174579f6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_ba53d154d498d2821d376ba1f49f7fc1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":2,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0216f55b04efd23ea90cc8c1b651a511","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":3,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_958957016e1944702298a2f2253cd33f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Sequential Memory Unit: Building an RNN Cell","order":4,"contentType":"PROBLEM","contentSlug":"recurrent-neural-network-cell","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_e324f0a9415b135764a389093be1f3ff","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Sequence models","sectionOrder":8,"title":"Recurrent Network with Temporal Gradient Learning","order":5,"contentType":"PROBLEM","contentSlug":"recurrent-network-temporal-gradient-learning","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_93274150806e24b52074cafc93134800","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_a658fbb6a1ef02163602cf7e1f493a33","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Linear Unit with Swish Activation","order":2,"contentType":"VISUALIZER","contentSlug":"swiglu-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_eb66bacb9a21462b23859c660efefffe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Linear Unit with Swish Activation","order":3,"contentType":"PROBLEM","contentSlug":"swiglu-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_806c97e66d42b9c114f25d163c3be64f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Head Pruning for Model Compression","order":4,"contentType":"PROBLEM","contentSlug":"attention-head-pruning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_702929440ba3c51f46e5f72ed22c2628","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Attention Score Clipping Stabilizer","order":5,"contentType":"PROBLEM","contentSlug":"attention-score-clipping-stabilizer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d4e2a5d014ea89b279cdc6f9d8090d22","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Causal Attention Mechanism","order":6,"contentType":"PROBLEM","contentSlug":"causal-attention-mechanism","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_28d74420daeefb237ec02cb321ee772b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Two-Layer Feed-Forward Network with Skip Connection and Dropout","order":7,"contentType":"PROBLEM","contentSlug":"feedforward-residual-block","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0fcc67dbdcc5503491a200af38905f0f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Gated Attention Mechanism","order":8,"contentType":"PROBLEM","contentSlug":"gated-attention-mechanism","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b977df7a72ce1419ddbdcbfb2f20b80e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Localized Sliding Attention Mechanism","order":9,"contentType":"PROBLEM","contentSlug":"localized-sliding-attention","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d8f13d1e29c05e14273763444258c9f7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Scaled Dot-Product Attention","order":10,"contentType":"PROBLEM","contentSlug":"scaled-dot-product-attention","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_670ad63c5194722054a8f71ebc1785bc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Self-Attention Weighted Aggregation","order":11,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f63eacdfe6c6ae4c24c1f7f557cf1ac4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sequence-Wise Feature Normalization with Affine Transform","order":12,"contentType":"PROBLEM","contentSlug":"sequence-layer-normalization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_85bedfdefabb589697d29726acd50556","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sparse Mixture-of-Experts: Dynamic Expert Selection and Aggregation","order":13,"contentType":"PROBLEM","contentSlug":"sparse-moe-expert-selection","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c1110669e526e41f63d47c0d51191af8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Autoregressive Text Synthesis Engine","order":14,"contentType":"PROBLEM","contentSlug":"autoregressive-text-synthesis-engine","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0fcae7e31875bd39e87bb69fb1a68228","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Memory-Efficient Tiled Attention Forward Pass","order":15,"contentType":"PROBLEM","contentSlug":"memory-efficient-tiled-attention","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_83516eb68594d2d1dadc657c1458eacc","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Parallel Attention Heads Mechanism","order":16,"contentType":"PROBLEM","contentSlug":"parallel-attention-heads","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_cdd8b28151acf4819ab9f5bf12270d37","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Sparse Mixture of Experts Forward Pass","order":17,"contentType":"PROBLEM","contentSlug":"sparse-mixture-of-experts-layer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_137913d5151d76195708e9a5217cd078","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Attention and transformers","sectionOrder":9,"title":"Transformer Position Embedding Generator","order":18,"contentType":"PROBLEM","contentSlug":"transformer-position-embedding-generator","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_9592d0f9c2298c263eb144f1fa1cbd7a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_a1e329dc598f74898f3455ba5ff8fd6c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Bidirectional Diffusion Transformation","order":2,"contentType":"VISUALIZER","contentSlug":"bidirectional-diffusion-transformation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_bdef5ac0508c6e61dc113a2295d4a85c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Bidirectional Diffusion Transformation","order":3,"contentType":"PROBLEM","contentSlug":"bidirectional-diffusion-transformation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_31900c007005e4b78ddb6fce7af4d2e9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Diffusion Model Denoising Reconstruction Error","order":4,"contentType":"PROBLEM","contentSlug":"diffusion-denoising-reconstruction-error","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_04f600711ee7632b27ed8363d7293b31","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Diffusion Noise Propagation","order":5,"contentType":"PROBLEM","contentSlug":"diffusion-noise-propagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ec928614cfedc037de80e554355cf03c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Optimal Game Move Strategy","order":6,"contentType":"PROBLEM","contentSlug":"optimal-game-move-strategy","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_a96c66111fc8c3c56e1e6a3338ae833c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Generative models","sectionOrder":10,"title":"Adversarial Network for Gaussian Distribution Synthesis","order":7,"contentType":"PROBLEM","contentSlug":"adversarial-network-gaussian-synthesis","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7d09485c35a830c32d1c11d2c6a549bb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_c2adbae43cbe24000b4872a2dc57b11b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Cumulative Reward with Temporal Decay","order":2,"contentType":"VISUALIZER","contentSlug":"cumulative-reward-with-decay","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1008aa325b59e159096b646189e3f6ba","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Cumulative Reward with Temporal Decay","order":3,"contentType":"PROBLEM","contentSlug":"cumulative-reward-with-decay","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_66afc79a41950f75d1807f303c0eba7e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Exploration Bonus Action Selector","order":4,"contentType":"PROBLEM","contentSlug":"exploration-bonus-action-selector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5074de0ef184e1f86658cec1c7008dc0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Intra-Group Advantage Normalization for Policy Training","order":5,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_06765e3986ec0390af33ae39c3238190","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":6,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2d0a577a61d4c4b034524393796e03ee","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Sample Accuracy and Consensus Voting Metrics","order":7,"contentType":"PROBLEM","contentSlug":"sample-accuracy-consensus-voting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_38caafedb9bbf78603c6ae2f7ec7e728","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Streaming Average Update for Real-Time Estimation","order":8,"contentType":"PROBLEM","contentSlug":"streaming-average-update","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2aac07bb304082ef1d9c9ffa43db31b6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Bellman Prediction Error for State Value Updates","order":9,"contentType":"PROBLEM","contentSlug":"temporal-difference-error","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_1d271ab1f2f46a8eb25ae5619a672955","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Trajectory Cumulative Discounted Reward","order":10,"contentType":"PROBLEM","contentSlug":"trajectory-cumulative-discounted-reward","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_37f508803c435594daeac64f33d7edf2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Adversarial Critic Reward Computation","order":11,"contentType":"PROBLEM","contentSlug":"adversarial-critic-reward-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f8c570344424f21bb461a0e2a65c7d13","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Token-Budget Aware Reinforcement Learning Loss","order":12,"contentType":"PROBLEM","contentSlug":"budget-aware-rl-loss","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_0452073c4dce23c7e260087919297866","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Exploration-Exploitation Action Chooser","order":13,"contentType":"PROBLEM","contentSlug":"exploration-exploitation-action-chooser","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3cb269b402c08d7c5e09c3c6acdd13a4","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Grid Navigation Value Estimation","order":14,"contentType":"PROBLEM","contentSlug":"grid-navigation-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_c6fde09daa7d32998d81894e7fbae534","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Initial Encounter Value Estimation from Episodic Experience","order":15,"contentType":"PROBLEM","contentSlug":"initial-encounter-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_5ff93fb2b158eae4d2816ba11120dd8a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Action Value Estimation in Markov Decision Processes","order":16,"contentType":"PROBLEM","contentSlug":"mdp-action-value-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7d7a16f03e4f637155c42ca0cd63cfc8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Multi-Step Value Estimation","order":17,"contentType":"PROBLEM","contentSlug":"multi-step-value-estimation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d56a313b2117be8b78f80437090e870d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"On-Policy Temporal Difference Learning for Action-Value Estimation","order":18,"contentType":"PROBLEM","contentSlug":"on-policy-temporal-difference-learning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_aeca24f3c0a7af44a85a1eaaf475e981","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Preference-Based Action Selector with Softmax Probabilities","order":19,"contentType":"PROBLEM","contentSlug":"preference-based-action-selector","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_fb601350b7378f76abdb4c82a44aa974","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Recency Weighted Value Estimate","order":20,"contentType":"PROBLEM","contentSlug":"recency-weighted-value-estimate","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_ecff14f5ea50e956a1634213f48bf34d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"State-Value Function Bellman Update","order":21,"contentType":"PROBLEM","contentSlug":"state-value-bellman-update","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_35c0c9142b9f15c8292c4ba4fe528e93","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Temporal Difference Value Estimator for Sequential Decision Problems","order":22,"contentType":"PROBLEM","contentSlug":"temporal-difference-value-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8af275239547b1851f9cf7cd84dace10","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Unbiased Policy Gradient Objective Computation","order":23,"contentType":"PROBLEM","contentSlug":"unbiased-policy-gradient-objective","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_95be1280288166c72d82b11f6ce127f6","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Coin Flip Wealth Strategy Optimizer","order":24,"contentType":"PROBLEM","contentSlug":"coin-flip-wealth-strategy-optimizer","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7362f58c845d170d1319c221f248dd39","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Group Policy Optimization Objective","order":25,"contentType":"PROBLEM","contentSlug":"group-policy-optimization-objective","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_8e1df7f5e4a53975416749242a3f633c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Policy Gradient Computation via REINFORCE","order":26,"contentType":"PROBLEM","contentSlug":"reinforce-policy-gradient-estimator","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_02a6f63c7c935e11375b4f2dc00ad57a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Sequence-Level Policy Gradient Objective","order":27,"contentType":"PROBLEM","contentSlug":"sequence-level-policy-gradient-objective","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_2a9a284060bc9d603c8552e64abc3a28","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"Simulation-Based Game Tree Exploration","order":28,"contentType":"PROBLEM","contentSlug":"simulation-based-game-tree-exploration","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_eebbbc6330ca399cc0288e887f3f47cb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Reinforcement learning","sectionOrder":11,"title":"TD(λ) Value Estimation with Eligibility Traces","order":29,"contentType":"PROBLEM","contentSlug":"temporal-difference-lambda-eligibility","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_d676092bad7018bc9087a3c1dc45898c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","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_9dcee77fc280f1ed22c83796b6838ff5","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Average Absolute Deviation Score","order":2,"contentType":"VISUALIZER","contentSlug":"average-absolute-deviation-score","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7aa87a6350f3ca4004e7befd673e8396","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Average Absolute Deviation Score","order":3,"contentType":"PROBLEM","contentSlug":"average-absolute-deviation-score","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_b711c804876abba08aefb8a9a32de368","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Batch Inference Health Monitor","order":4,"contentType":"PROBLEM","contentSlug":"batch-inference-health-monitor","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_f8967d30618aa27cf8dae7f82c668d11","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classification Outcome Matrix","order":5,"contentType":"PROBLEM","contentSlug":"binary-classification-outcome-matrix","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_609e5ff5433bc9026086b3dd5f203e6c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Overlap Coefficient Calculation","order":6,"contentType":"PROBLEM","contentSlug":"binary-overlap-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3ef5423ef389c3590c5fed992335e132","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Set Similarity Coefficient","order":7,"contentType":"PROBLEM","contentSlug":"binary-set-similarity-coefficient","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_7b2de8a086a3ee8cccab1fe179e9079e","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Accuracy Metric","order":8,"contentType":"PROBLEM","contentSlug":"classification-accuracy-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_45f5ae8a620787e53289fef3963d19aa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Coefficient of Determination Calculator","order":9,"contentType":"PROBLEM","contentSlug":"coefficient-of-determination-calculator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_70f57f04de358ec583f370b4f10e40bf","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Conditional Probability Estimation from Categorical Observations","order":10,"contentType":"PROBLEM","contentSlug":"conditional-probability-estimation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_9deb5cc02273303750cb2e58625f15b2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Statistical Divergence Between Probability Distributions","order":11,"contentType":"PROBLEM","contentSlug":"distribution-divergence-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_cfe4c8701ae55e6033bd0d0bb9daad52","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Computing Gini Impurity for Classification Labels","order":12,"contentType":"PROBLEM","contentSlug":"gini-impurity-calculation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.201Z","updatedAt":"2026-10-17T16:28:32.743Z","pages":[]},{"id":"seed_3971d49ba131dbdc0149ba63531a4f5b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Harmonic Balance Metric for Binary Classification","order":13,"contentType":"PROBLEM","contentSlug":"harmonic-balance-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_9207dc59de2264fe08755a98054ebf4f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Weighted Harmonic Mean of Precision and Recall","order":14,"contentType":"PROBLEM","contentSlug":"harmonic-precision-recall-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_25ac20a8bb9ff19c9a5d4e7105cee135","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Inference Performance Metrics Calculator","order":15,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_244b3c5ba5c392a7df31651aed18a0b8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Language Model Perplexity Score","order":16,"contentType":"PROBLEM","contentSlug":"language-model-perplexity-score","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_ee227ad28c7cc42b95b1b2fc11c8a634","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Model Fit Diagnosis: Identifying Generalization Issues","order":17,"contentType":"PROBLEM","contentSlug":"model-fit-diagnosis","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_a24bc8b7ed87ef5cd7f075a4d491c02d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Compute Service Level Agreement Metrics for ML Inference Endpoints","order":18,"contentType":"PROBLEM","contentSlug":"model-service-sla-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_c64f7cb2098b117180b55ef5909a1905","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Precision Evaluation Metric for Binary Classification","order":19,"contentType":"PROBLEM","contentSlug":"precision-evaluation-metric","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_86278bda4af7b507dd31acbf55ab9f42","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Prediction Error Magnitude Calculator","order":20,"contentType":"PROBLEM","contentSlug":"prediction-error-magnitude","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_bc47c21ceb3d43a6df32f0bff5c5eb9d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Sensitivity Score for Binary Classifier","order":21,"contentType":"PROBLEM","contentSlug":"sensitivity-score-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_0d515fcb59707c890ddc6176e5228278","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Service Latency Percentile Metrics","order":22,"contentType":"PROBLEM","contentSlug":"service-latency-percentile-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_696a2bdbb7fdccca1e480b888b493200","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Text Accuracy Evaluator with Normalization","order":23,"contentType":"PROBLEM","contentSlug":"text-accuracy-evaluator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_00ba10c9be865a58e4e1782e1f9022b3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Vector Angular Similarity Measure","order":24,"contentType":"PROBLEM","contentSlug":"vector-angular-similarity-measure","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_c679d53ca0e7a542ed486e0212f90213","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classifier Evaluation Suite","order":25,"contentType":"PROBLEM","contentSlug":"binary-classifier-evaluation-suite","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_b006e4f1825efddd2ca9c4770518309c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classifier Performance Curve","order":26,"contentType":"PROBLEM","contentSlug":"binary-classifier-performance-curve","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_8ad0a9155d2470ff78ce257d9701966c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Feature Probabilistic Classifier","order":27,"contentType":"PROBLEM","contentSlug":"binary-feature-probabilistic-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_2a72e24b11adbbe8f488f75690dfed8f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Canary Deployment Health Analysis for ML Model Rollouts","order":28,"contentType":"PROBLEM","contentSlug":"canary-deployment-health-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_617f68e744814a20f916e46a15cdc9d7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Sample Mean Convergence to Gaussian Distribution","order":29,"contentType":"PROBLEM","contentSlug":"central-limit-theorem-simulation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_790f9bb78d594a266e50f14923adf07b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Classification Outcome Matrix Builder","order":30,"contentType":"PROBLEM","contentSlug":"classification-outcome-matrix-builder","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_298ba9aade73fbb7373712161d230a13","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Cluster Cohesion Coefficient for Clustering Quality Assessment","order":31,"contentType":"PROBLEM","contentSlug":"cluster-cohesion-coefficient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_057cf133aa54e62b9a10cb03bc155e73","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Code Output Validator for Automated Assessment","order":32,"contentType":"PROBLEM","contentSlug":"code-output-validator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_d83e33ef8a65572febe8f612ab7eaded","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Data Partitioning with Cross-Validation Splits","order":33,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_d92b0d0a435c6a5681e182122cc5e694","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Dataset Integrity Metrics for Production ML Systems","order":34,"contentType":"PROBLEM","contentSlug":"dataset-integrity-metrics","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_e0cff58f1257bdd529d2430fa4a6fed8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Distribution Stability Analysis Using Stability Divergence Index","order":35,"contentType":"PROBLEM","contentSlug":"distribution-stability-analysis","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_2cb719335d74c8e6d1024c50834be547","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Inference Request Batching","order":36,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_1b0e83d4d4b81d1463a23f5d576f8a27","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"LaTeX Delimited Answer Extraction","order":37,"contentType":"PROBLEM","contentSlug":"latex-delimited-answer-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_f1fe44c115984737f090efb35b79f5f9","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Mathematical Expression Equivalence Validator","order":38,"contentType":"PROBLEM","contentSlug":"mathematical-expression-equivalence-validator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_70d9f8ebb3e65685c01cf122b9a9d1eb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Binary Classification Quality Score Using Matthews Correlation","order":39,"contentType":"PROBLEM","contentSlug":"matthews-correlation-coefficient","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_206bf5d2e2eb148c585bc5981b29f3bb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Multi-Evaluator Quality Assessment Score","order":40,"contentType":"PROBLEM","contentSlug":"multi-evaluator-quality-assessment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_247324f5264af1d5a45f7c3d3610e872","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Multiple Choice Log-Probability Evaluation","order":41,"contentType":"PROBLEM","contentSlug":"multiple-choice-log-probability-evaluation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_ee87874da7e8b9d0c93bebcc883800d0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Pairwise Ranking Score Adjustment for Competitive Models","order":42,"contentType":"PROBLEM","contentSlug":"pairwise-ranking-score-adjustment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_c272453b5d2c4118b25ddbedc6777dad","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Pairwise Ranking Strength Estimator","order":43,"contentType":"PROBLEM","contentSlug":"pairwise-ranking-strength-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_1d669755173e1c884281d68b2361eb05","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Population Mean Confidence Estimator","order":44,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_7a6a5e3aba0fe03692f99ef9952f827f","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Posterior Parameter Estimation with Conjugate Prior","order":45,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_3d36411b86ef1db8a500c42b53203d0d","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Precision-Recall Curve for Binary Classification","order":46,"contentType":"PROBLEM","contentSlug":"precision-recall-curve-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_d60565d52a857e179a5e994d0ba7fcb3","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Model Prediction Distribution Drift Detector","order":47,"contentType":"PROBLEM","contentSlug":"prediction-distribution-monitoring","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_2f8aa1abc43b9af476c541dd05c20afe","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Probabilistic Gaussian Classifier","order":48,"contentType":"PROBLEM","contentSlug":"probabilistic-gaussian-classifier","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_b0ae48e1f5413845019a40ddae343c76","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Probability Calibration Assessment","order":49,"contentType":"PROBLEM","contentSlug":"probability-calibration-assessment","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_85968379bbe6f46ee6d54c5eaf401b96","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Proximity-Based Neighbor Retrieval","order":50,"contentType":"PROBLEM","contentSlug":"proximity-based-neighbor-retrieval","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_58735bc79c28646ee74eb1bef15995c0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Receiver Operating Characteristic Curve Area Computation","order":51,"contentType":"PROBLEM","contentSlug":"receiver-operating-characteristic-area","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_ae2b3e511cd9550cf02edb2a4f3a308b","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Symmetric 8-Bit Integer Quantization","order":52,"contentType":"PROBLEM","contentSlug":"symmetric-8bit-integer-quantization","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_570bf4d7a6920c611b8071c5bc335042","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"N-Gram Precision Score for Text Generation Evaluation","order":53,"contentType":"PROBLEM","contentSlug":"text-generation-quality-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_4543254e3612e9220968e6dac41e462a","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Translation Alignment Metric for Quality Assessment","order":54,"contentType":"PROBLEM","contentSlug":"translation-alignment-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_5709e17c375dfaada45ac8b7d35b9594","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Unigram Overlap Evaluation Metric","order":55,"contentType":"PROBLEM","contentSlug":"unigram-overlap-evaluation-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_83c91368fde51847eb9c7d380d514ce0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Variance Ratio Criterion for Cluster Quality Assessment","order":56,"contentType":"PROBLEM","contentSlug":"variance-ratio-clustering-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_8b640f246b265f3705dabaeb2f785232","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Weighted Model Response Evaluator","order":57,"contentType":"PROBLEM","contentSlug":"weighted-model-response-evaluator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_c888b913f332e0c96f9d76e93296fccd","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Approximate Posterior Quality Metric via Monte Carlo Sampling","order":58,"contentType":"PROBLEM","contentSlug":"variational-bound-estimation","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_4ed553fb5a4ee697e73c7bc8a8413602","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"ML serving and evaluation","sectionOrder":12,"title":"Workflow Dependency Scheduler with Critical Path Analysis","order":59,"contentType":"PROBLEM","contentSlug":"workflow-dependency-scheduler-critical-path","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_1cf3ca25b4d85e36ac916fe9bf291c5c","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","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_aade27e9f399c96b30b15498a3677c02","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Single Word Frequency Likelihood","order":2,"contentType":"VISUALIZER","contentSlug":"single-word-frequency-likelihood","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_2b5dfacb2bc14598853faa7c10b40eb2","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Single Word Frequency Likelihood","order":3,"contentType":"PROBLEM","contentSlug":"single-word-frequency-likelihood","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_cbd9fbefb3f64bf7ef0dfad6c77ccfeb","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Association Strength Metric Between Events","order":4,"contentType":"PROBLEM","contentSlug":"association-strength-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_3bf818df30b9d1a285616a8d21b04ff8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Optimal String Edit Metric","order":5,"contentType":"PROBLEM","contentSlug":"optimal-string-edit-metric","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_b244395d84a26e25343469f65aa46024","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Probabilistic Document Relevance Scorer (BM25)","order":6,"contentType":"PROBLEM","contentSlug":"probabilistic-relevance-scorer","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_a54be1839791d24df220d15a475d56a0","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Text and information retrieval","sectionOrder":13,"title":"Computing Document Term Relevance Scores","order":7,"contentType":"PROBLEM","contentSlug":"term-frequency-inverse-document-frequency","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_01e679cf3875844c326b1d46cdda1c61","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","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_1e619df8a6869c66c86dffd02326e6ca","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Portfolio Risk Variance Computation","order":2,"contentType":"VISUALIZER","contentSlug":"portfolio-risk-variance-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_26f2d313e7d6e73a7337ca91d76256a1","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Portfolio Risk Variance Computation","order":3,"contentType":"PROBLEM","contentSlug":"portfolio-risk-variance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_0ddbdd932939ea21de91ea9e4554afef","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Monetary Volume Aggregated Bars","order":4,"contentType":"PROBLEM","contentSlug":"monetary-volume-aggregated-bars","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_e37069aafdb57e221cd2889f3b150ffa","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Transaction-Based Bar Aggregation","order":5,"contentType":"PROBLEM","contentSlug":"transaction-based-bar-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_02ca952bebd85261b44ff1b91fbc85b8","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Financial time series","sectionOrder":14,"title":"Volume-Based Candlestick Aggregation","order":6,"contentType":"PROBLEM","contentSlug":"volume-based-candlestick-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_89f740cbcf7a1aec02b5744bb0d900ce","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","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_369219c088ffe96e3d00574dd39e4a46","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"Frequent Pattern Discovery in Transaction Data","order":2,"contentType":"VISUALIZER","contentSlug":"frequent-pattern-discovery","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]},{"id":"seed_c2975623377895773b4dd6b3dfafa2e7","learningPathId":"seed_7f6b8faa6086f2b6271ead87974bea32","section":"Additional numerical and ML techniques","sectionOrder":15,"title":"Frequent Pattern Discovery in Transaction Data","order":3,"contentType":"PROBLEM","contentSlug":"frequent-pattern-discovery","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.238Z","updatedAt":"2026-10-17T16:28:33.033Z","pages":[]}]},"currentModule":"$46:props:state:queries:1:state:data:json:path:modules:132","prevModule":"$46:props:state:queries:1:state:data:json:path:modules:131","nextModule":"$46:props:state:queries:1:state:data:json:path:modules:133","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"],"path.modules.328.createdAt":["Date"],"path.modules.328.updatedAt":["Date"],"path.modules.329.createdAt":["Date"],"path.modules.329.updatedAt":["Date"],"path.modules.330.createdAt":["Date"],"path.modules.330.updatedAt":["Date"],"path.modules.331.createdAt":["Date"],"path.modules.331.updatedAt":["Date"],"path.modules.332.createdAt":["Date"],"path.modules.332.updatedAt":["Date"],"path.modules.333.createdAt":["Date"],"path.modules.333.updatedAt":["Date"],"path.modules.334.createdAt":["Date"],"path.modules.334.updatedAt":["Date"],"path.modules.335.createdAt":["Date"],"path.modules.335.updatedAt":["Date"],"path.modules.336.createdAt":["Date"],"path.modules.336.updatedAt":["Date"],"path.modules.337.createdAt":["Date"],"path.modules.337.updatedAt":["Date"],"path.modules.338.createdAt":["Date"],"path.modules.338.updatedAt":["Date"],"path.modules.339.createdAt":["Date"],"path.modules.339.updatedAt":["Date"],"path.modules.340.createdAt":["Date"],"path.modules.340.updatedAt":["Date"],"path.modules.341.createdAt":["Date"],"path.modules.341.updatedAt":["Date"],"path.modules.342.createdAt":["Date"],"path.modules.342.updatedAt":["Date"],"path.modules.343.createdAt":["Date"],"path.modules.343.updatedAt":["Date"],"path.modules.344.createdAt":["Date"],"path.modules.344.updatedAt":["Date"],"path.modules.345.createdAt":["Date"],"path.modules.345.updatedAt":["Date"],"path.modules.346.createdAt":["Date"],"path.modules.346.updatedAt":["Date"],"currentModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.132.createdAt","currentModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.132.updatedAt","prevModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.131.createdAt","prevModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.131.updatedAt","nextModule.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.133.createdAt","nextModule.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.133.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","allModules.328.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.328.createdAt","allModules.328.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.328.updatedAt","allModules.329.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.329.createdAt","allModules.329.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.329.updatedAt","allModules.330.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.330.createdAt","allModules.330.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.330.updatedAt","allModules.331.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.331.createdAt","allModules.331.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.331.updatedAt","allModules.332.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.332.createdAt","allModules.332.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.332.updatedAt","allModules.333.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.333.createdAt","allModules.333.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.333.updatedAt","allModules.334.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.334.createdAt","allModules.334.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.334.updatedAt","allModules.335.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.335.createdAt","allModules.335.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.335.updatedAt","allModules.336.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.336.createdAt","allModules.336.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.336.updatedAt","allModules.337.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.337.createdAt","allModules.337.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.337.updatedAt","allModules.338.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.338.createdAt","allModules.338.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.338.updatedAt","allModules.339.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.339.createdAt","allModules.339.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.339.updatedAt","allModules.340.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.340.createdAt","allModules.340.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.340.updatedAt","allModules.341.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.341.createdAt","allModules.341.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.341.updatedAt","allModules.342.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.342.createdAt","allModules.342.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.342.updatedAt","allModules.343.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.343.createdAt","allModules.343.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.343.updatedAt","allModules.344.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.344.createdAt","allModules.344.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.344.updatedAt","allModules.345.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.345.createdAt","allModules.345.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.345.updatedAt","allModules.346.createdAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.346.createdAt","allModules.346.updatedAt":"$46:props:state:queries:1:state:data:meta:values:path.modules.346.updatedAt"},"referentialEqualities":{"allModules":["path.modules"],"prevModule":["path.modules.131"],"currentModule":["path.modules.132"],"nextModule":["path.modules.133"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792296764089,"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-patterns","contentType":"LEARNING","contentSlug":"feature-engineering-mastery"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getModule\"],{\"input\":{\"contentSlug\":\"feature-engineering-mastery\",\"contentType\":\"LEARNING\",\"pathSlug\":\"machine-learning-patterns\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764340,"state":{"data":{"json":{"id":"seed_c514e98b204bd1219affb5d0783386a7","slug":"feature-engineering-mastery","category":"Machine Learning","topic":"Feature Engineering & Selection","title":"Feature Engineering Mastery","description":"Master the art and science of feature engineering—the crucial bridge between raw data and powerful machine learning models. Learn to craft features that capture domain knowledge, create meaningful interactions, handle temporal patterns, and transform text and categorical data into model-ready representations.","difficulty":"INTERMEDIATE","estimatedMinutes":90,"pages":[{"id":"seed_56dd58a33d5534516199c7580e841f63","title":"Feature Types","order":0,"content":[{"id":"opening-hook","title":"The Atoms of Machine Learning","content":[{"data":{"content":"In chemistry, understanding atomic properties determines how elements combine into molecules. In machine learning, understanding **feature types** determines how data transforms into predictions. Features are the atomic units of machine learning—the individual measurements, attributes, and characteristics that models consume to make decisions.\n\nYet many practitioners treat features as interchangeable inputs, feeding whatever data they have into models without considering the fundamental nature of each variable. This approach leaves enormous predictive power on the table and often leads to subtle bugs that silently degrade model performance."},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will have a rigorous understanding of feature type taxonomy—numerical, categorical, ordinal, binary, and derived features. You'll know how each type behaves mathematically, what encoding strategies they require, and which modeling assumptions they satisfy or violate. This foundation is essential for everything that follows in feature engineering.","variant":"info"},"type":"callout"}]},{"id":"feature-type-taxonomy","title":"The Feature Type Taxonomy","content":[{"data":{"content":"Before diving into individual feature types, we need to understand why this taxonomy matters. The type of a feature determines:\n\n- **Mathematical operations** that are valid (can you compute a mean?)\n- **Distance metrics** that are meaningful (is Euclidean distance sensible?)\n- **Encoding strategies** required for model consumption\n- **Preprocessing steps** needed (scaling, normalization, imputation)\n- **Model compatibility** (tree-based vs. linear vs. neural approaches)\n\nGetting feature types wrong doesn't cause obvious errors—models still train and produce predictions. But those predictions are based on mathematically incoherent operations, leading to degraded performance that's difficult to diagnose."},"type":"text"},{"data":{"rows":[["**Numerical (Continuous)**","Ordered, arithmetic meaningful, infinite precision theoretically","Temperature, price, height, duration","Scale-sensitive; requires normalization for many algorithms"],["**Numerical (Discrete)**","Ordered, countable, integer-valued","Age in years, count of items, number of children","May need binning; consider as continuous or categorical depending on cardinality"],["**Categorical (Nominal)**","Unordered classes, no arithmetic meaning","Country, color, product category, user ID","Requires encoding; high cardinality is challenging"],["**Categorical (Ordinal)**","Ordered classes, intervals not equal","Education level, satisfaction rating, size (S/M/L)","Order matters but magnitude doesn't; encoding must preserve order"],["**Binary**","Two mutually exclusive states","Yes/No, True/False, Male/Female, Purchased/Not","Special case of categorical; simple 0/1 encoding usually sufficient"],["**Derived/Composite**","Created from other features","Ratios, interactions, aggregations, embeddings","Type depends on construction; often where domain expertise shines"]],"caption":"Feature Type Taxonomy Overview","headers":["Feature Type","Mathematical Properties","Examples","Key Considerations"]},"type":"table"},{"data":{"icon":"AlertCircle","title":"Type Ambiguity is Common","content":"Real-world features often don't fit cleanly into one category. A 'star rating' (1-5) could be treated as ordinal, discrete numerical, or even continuous after averaging. Zip codes are numerical in representation but categorical in meaning. Always consider the semantic meaning, not just the data format.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"numerical-features","title":"Numerical Features: Continuous and Discrete","content":[{"data":{"content":"Numerical features represent quantities where arithmetic operations are meaningful. The distinction between continuous and discrete is theoretically important but practically blurred—most algorithms treat them identically after preprocessing.\n\n**Continuous Numerical Features**\n\nContinuous features can theoretically take any value within a range. Examples include:\n- Physical measurements: height (cm), weight (kg), temperature (°C)\n- Financial values: price ($), revenue, stock returns\n- Temporal durations: seconds since event, session length\n- Derived metrics: click-through rate, conversion probability\n\nThese features have several key properties:"},"type":"text"},{"data":{"items":["**Arithmetic is meaningful** — Mean, standard deviation, and differences convey information. A temperature of 20°C is genuinely 'between' 10°C and 30°C.","**Scale matters** — A feature ranging 0-1 behaves differently than one ranging 0-1,000,000. Most algorithms are scale-sensitive.","**Distribution shape affects modeling** — Heavily skewed distributions often benefit from log or power transforms.","**Outliers have disproportionate influence** — Extreme values can dominate distance calculations and gradient updates.","**Missing values require careful imputation** — Mean, median, or model-based imputation each has tradeoffs."],"title":{"icon":"Zap","text":"Properties of Continuous Features","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Discrete Numerical Features**\n\nDiscrete features take countable values, typically integers:\n- Counts: number of purchases, page views, children\n- Rankings: position in search results, leaderboard rank\n- Indices: day of week (1-7), month (1-12), hour (0-23)\n\nThe key question with discrete numerics is **cardinality**:"},"type":"text"},{"data":{"rows":[["Very low (2-5)","Often better as categorical","Number of bedrooms"],["Low (5-20)","Could go either way","Age in decades"],["Medium (20-100)","Usually treat as numeric","Age in years"],["High (100+)","Almost always numeric","Days since signup"]],"headers":["Cardinality","Treatment Strategy","Example"]},"type":"table"},{"data":{"fileName":"numerical_feature_analysis.py","languages":[{"code":"$72","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Lightbulb","title":"The Scale Normalization Decision","content":"For tree-based models (Random Forest, XGBoost), scaling is unnecessary—trees split on thresholds regardless of scale. For linear models, SVMs, neural networks, and distance-based methods (k-NN, k-means), scaling is critical. When in doubt, standardize (z-score) or min-max normalize, but be aware that both approaches handle outliers differently.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"categorical-features","title":"Categorical Features: Nominal and Ordinal","content":[{"data":{"content":"Categorical features represent discrete classes or groups. Unlike numerical features, arithmetic operations are meaningless—you cannot average 'red' and 'blue.' The fundamental distinction within categoricals is whether an **order** exists.\n\n**Nominal (Unordered) Categorical Features**\n\nNominal features have no inherent ordering. Any ordering you impose is arbitrary:\n- Geographic: country, city, region, timezone\n- Identity: user_id, product_sku, session_id\n- Classification: animal species, disease type, error category\n- Descriptive: color, brand, payment method\n\nThe critical insight: **assigning integers to categories implies a false ordering**. If you encode `{red: 1, blue: 2, green: 3}`, a linear model will treat green as 'more than' blue, which is semantically meaningless."},"type":"text"},{"data":{"items":["**One-Hot Encoding** — Creates binary column per category. Safe default but explodes dimensionality for high cardinality. Use for <50 categories typically.","**Dummy Encoding** — One-hot minus one column (reference category). Prevents multicollinearity in linear models. Preferred for regression.","**Target Encoding** — Replaces category with mean of target variable. Powerful but risks overfitting—requires careful regularization and cross-validation.","**Frequency Encoding** — Replaces category with its frequency in training data. Simple and often effective for high-cardinality features.","**Hashing Trick** — Hashes categories to fixed-size vector. Handles unseen categories but introduces collisions. Good for very high cardinality.","**Embedding Layers** — Learns dense vector representation. Best for neural networks with high cardinality (user_id, product_id)."],"title":{"icon":"Target","text":"Nominal Feature Encoding Strategies","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Ordinal Categorical Features**\n\nOrdinal features have meaningful order but non-uniform intervals:\n- Education: high school < bachelor's < master's < PhD\n- Satisfaction: very unsatisfied < unsatisfied < neutral < satisfied < very satisfied\n- Size: small < medium < large < extra-large\n- Quality grade: F < D < C < B < A\n\nThe challenge: the **gaps between categories are semantically unequal**. The jump from 'unsatisfied' to 'neutral' might be psychologically larger than 'satisfied' to 'very satisfied.'"},"type":"text"},{"data":{"rows":[["**Integer Encoding**","Map to 0, 1, 2, ..., k-1","✓ Yes","✗ Assumes equal","Tree models; quick baseline"],["**Custom Mapping**","Domain-informed numeric values","✓ Yes","~ Approximated","When domain knowledge suggests intervals"],["**Target-Based Ordering**","Order by target correlation","✓ Yes","~ Data-driven","When order is uncertain or task-specific"],["**Treat as Nominal**","One-hot encoding","✗ Lost","N/A","When interval uncertainty is high; linear models"]],"caption":"Ordinal Encoding Strategies","headers":["Strategy","Description","Preserves Order","Preserves Intervals","Use Case"]},"type":"table"},{"data":{"fileName":"categorical_encoding.py","languages":[{"code":"$73","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"AlertCircle","title":"The High-Cardinality Challenge","content":"Features like user_id, product_sku, or IP address can have millions of unique values. One-hot encoding is impractical (millions of columns). Target encoding risks overfitting (some categories appear once). Hashing causes collisions. Embeddings require neural architectures. There's no perfect solution—choose based on model type, data volume, and acceptable complexity.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"binary-features","title":"Binary Features: The Special Case","content":[{"data":{"content":"Binary features represent exactly two mutually exclusive states. While technically categorical, they deserve special treatment due to their simplicity and ubiquity.\n\n**Common Binary Feature Types:**"},"type":"text"},{"data":{"rows":[["Boolean flags","is_verified, has_subscription, email_confirmed"],["Presence/absence","clicked, purchased, churned, converted"],["Binary classification","pass/fail, spam/ham, positive/negative"],["Demographic","gender (when binary), citizen/non-citizen"],["Threshold-based","high_value_customer, above_median_income"]],"headers":["Category","Examples"]},"type":"table"},{"data":{"content":"**Encoding is trivial**: 0/1 representation works for virtually all algorithms. The main considerations are semantic clarity and handling edge cases."},"type":"text"},{"data":{"items":["**Consistent semantics** — Decide whether 1 means 'positive' or 'present' and stick with it. Don't mix is_churned (1=bad) with is_active (1=good) without clear documentation.","**Handle missing values explicitly** — A missing 'has_subscription' might mean 'unknown' (impute or model) vs 'not applicable' (should be 0 or separate category).","**Consider class imbalance** — Binary features that are 99% one class carry little information for splits but may be critical for the 1% case.","**Watch for data leakage** — Binary labels created after observation time (is_churned_30days) can leak future information if not handled carefully.","**Aggregation context** — Mean of a binary feature gives the rate (click-through rate, conversion rate), which is often more useful than raw counts."],"title":{"icon":"CheckCircle","text":"Binary Feature Best Practices","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Creating Binary Features from Other Types**\n\nBinary features are often derived by thresholding or categorizing other feature types:\n\n```python\n# From numerical: thresholding\ndf['is_high_income'] = (df['income'] > 100000).astype(int)\ndf['is_adult'] = (df['age'] >= 18).astype(int)\n\n# From categorical: presence detection\ndf['is_premium_member'] = (df['membership_type'] == 'premium').astype(int)\ndf['is_us_based'] = df['country'].isin(['US', 'USA', 'United States']).astype(int)\n\n# From text: pattern matching\ndf['has_email'] = df['email'].notna().astype(int)\ndf['mentions_discount'] = df['review_text'].str.contains('discount|coupon|sale', case=False).astype(int)\n```\n\nThese derived binaries often capture domain-specific thresholds that are more predictive than the raw values."},"type":"text"}],"titleType":"number","titleNumber":4},{"id":"feature-type-relationships","title":"Feature Type Relationships and Conversions","content":[{"data":{"content":"Features types aren't fixed—they can be converted based on modeling needs. Understanding these conversions and their tradeoffs is essential for effective feature engineering.\n\n**Information Flow in Conversions:**\n\n```\nContinuous → Discrete → Ordinal → Nominal → Binary\n ↓ ↓ ↓ ↓ ↓\n (binning) (ordering) (collapse) (one-hot) (present)\n \nEach arrow represents INFORMATION LOSS.\nReverse arrows (binned → continuous) recover approximations, not originals.\n```"},"type":"text"},{"data":{"rows":[["Continuous","Discrete","Rounding, floor, ceiling","Loses precision; useful for counts or when precision is noise"],["Continuous","Ordinal","Quantile binning, equal-width binning","Loses magnitude; gains robustness to outliers"],["Continuous","Binary","Thresholding","Major information loss; captures only one split point"],["Discrete","Categorical","Treat values as labels","Loses numerical relationships; gains flexibility"],["Ordinal","Nominal","Ignore ordering","Loses order information; may be necessary for fair one-hot"],["Ordinal","Numerical","Integer coding","Assumes equal intervals (may be false)"],["Categorical","Binary (each)","One-hot encoding","Preserves all; explodes dimensionality"],["Categorical","Numerical","Target/frequency encoding","Summarizes via statistics; risks leakage"]],"caption":"Feature Type Conversion Matrix","headers":["From","To","Method","Information Impact"]},"type":"table"},{"data":{"icon":"Lightbulb","title":"When to Convert Types","content":"Convert continuous → binned when: outliers cause problems, relationship is non-linear and bins capture it, or interpretability matters. Convert categorical → numerical when: high cardinality makes one-hot impractical, target encoding reduces overfitting via smoothing, or embedding layers aren't available. Keep original features when possible—let the model discover the best representation.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"identifying-types","title":"Identifying Feature Types in Practice","content":[{"data":{"content":"Real datasets rarely come with explicit type annotations. The data format (int, float, string) is a clue but not definitive. Here's a systematic approach to type identification:"},"type":"text"},{"data":{"fileName":"feature_type_detection.py","languages":[{"code":"$74","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"items":["**Zip codes** — Stored as integers but semantically categorical. Arithmetic is meaningless (10001 + 10002 ≠ meaningful).","**Phone numbers, SSNs, IDs** — Numeric format but identifier semantics. Never treat as numerical.","**Dates as integers** — YYYYMMDD format needs datetime parsing, not numeric treatment.","**Boolean as 1/2 or Yes/No** — May need recoding to 0/1 for proper binary handling.","**Ratings stored as floats** — Often discrete ordinal despite float dtype (1.0, 2.0, 3.0...).","**Encoded categoricals** — Previous label encoding makes categoricals look numeric. Check data dictionary."],"title":{"icon":"AlertCircle","text":"Common Type Identification Pitfalls","type":"icon","color":"accent","variant":"outline-circle"},"variant":"glass"},"type":"list"}],"titleType":"number","titleNumber":6},{"id":"summary","title":"Summary: Mastering Feature Types","content":[{"data":{"content":"Feature types are the foundation of feature engineering. Getting them right enables all subsequent transformations; getting them wrong corrupts your entire modeling pipeline. Here are the key takeaways:"},"type":"text"},{"data":{"type":"ordered","items":["**Numerical features** allow arithmetic but require scaling for many algorithms. Distinguish continuous from discrete based on cardinality and domain meaning.","**Categorical features** require encoding. Nominal features need one-hot or similar; ordinal features need order-preserving encoding. High cardinality requires special techniques.","**Binary features** are simple but ubiquitous. Consistent semantics and careful missing value handling are essential.","**Type conversions trade information for structure**. Binning loses precision but gains robustness; encoding compresses categories but may distort relationships.","**Always verify types semantically**, not just by data format. Zip codes aren't numerical; ratings might be ordinal despite integer storage.","**When uncertain, try multiple representations**. Let model performance guide the choice between treating a feature as categorical vs. numerical."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now have a rigorous understanding of feature types—the atomic units of machine learning. Next, we'll explore how domain knowledge transforms raw features into powerful predictors that capture business and scientific understanding.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":7}],"isLocked":false},{"id":"seed_842f4a11ba72179bd359958a69c32a08","title":"Domain Knowledge","order":1,"content":null,"isLocked":true},{"id":"seed_0e74c9a08deb29e70b8fc92ba3fe99ed","title":"Interaction Features","order":2,"content":null,"isLocked":true},{"id":"seed_a46bdb1443418ed68ebb0bc3223d5a9d","title":"Time-Based Features","order":3,"content":null,"isLocked":true},{"id":"seed_09805d9c32a742bd2a525f9770161506","title":"Text and Categorical","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792296764163,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"feature-engineering-mastery"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"feature-engineering-mastery\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764343,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296764165,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_c514e98b204bd1219affb5d0783386a7"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_c514e98b204bd1219affb5d0783386a7\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764343,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296764165,"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_09805d9c32a742bd2a525f9770161506"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_09805d9c32a742bd2a525f9770161506\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764343,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296764165,"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_09805d9c32a742bd2a525f9770161506"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_09805d9c32a742bd2a525f9770161506\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764343,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792296764165,"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_09805d9c32a742bd2a525f9770161506"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_09805d9c32a742bd2a525f9770161506\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296764343,"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_09805d9c32a742bd2a525f9770161506","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_09805d9c32a742bd2a525f9770161506\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@75"}]},"children":"$L76"}]