46:"/_next/static/chunks/2g8w-80ml0qgh.js" 47:"/_next/static/chunks/0iv56yrji_20j.js" 48:"/_next/static/chunks/26v-2o8eqscfn.js" 49:"/_next/static/chunks/0lc44eilyyvky.js" 4a:"/_next/static/chunks/1jl6tco-uuy-j.js" 4b:"/_next/static/chunks/1ot1rbfa0d_n4.js" 4c:"/_next/static/chunks/3n7h0f3nk8y8p.js" 4d:"/_next/static/chunks/3k2gyg36fnag_.js" 4e:"/_next/static/chunks/3m0r7x0jt5wm7.js" 4f:"/_next/static/chunks/2fohwszm1xz1m.js" 50:"/_next/static/chunks/3bgoyiso1rhn7.js" 51:"/_next/static/chunks/19bc9syhv3w28.js" 52:"/_next/static/chunks/0wa9m1-tphf9u.js" 53:"/_next/static/chunks/0m2d8jznc9t02.js" 54:"/_next/static/chunks/1xu72k8i0p_ma.js" 55:"/_next/static/chunks/2a1rt036gjwtj.js" 56:"/_next/static/chunks/1s05-oinjbm85.js" 57:"/_next/static/chunks/20k3408cq3fwt.js" 58:"/_next/static/chunks/1_lo_qve3gu86.js" 59:"/_next/static/chunks/3di53f0fz7pw_.js" 5a:"/_next/static/chunks/3m1xjj5kw5fie.js" 5b:"/_next/static/chunks/0so1i35v7xcta.js" 5c:"/_next/static/chunks/082jo-0ajazgr.js" 5d:"/_next/static/chunks/0-a64pg0hn7g7.js" 5e:"/_next/static/chunks/09nq57stwzkgf.js" 5f:"/_next/static/chunks/20329i6m_04t6.js" 60:"/_next/static/chunks/0i3-eynrkvqi7.js" 61:"/_next/static/chunks/0oag6lffx7d01.js" 62:"/_next/static/chunks/1nyz-fz1mk3y6.js" 63:"/_next/static/chunks/1dl4k5870muic.js" 64:"/_next/static/chunks/2ozcrjb_hh74t.js" 65:"/_next/static/chunks/18ggtgz2yf759.js" 66:"/_next/static/chunks/1tn-0fivsxsv1.js" 67:"/_next/static/chunks/0hlb44ca7-4a2.js" 68:"/_next/static/chunks/1ch_-e7_eco00.js" 69:"/_next/static/chunks/40mq7ggq-ozo4.js" 6a:"/_next/static/chunks/2112y_x317i7a.js" 6b:"/_next/static/chunks/2k8pepwc4pxns.js" 6c:"/_next/static/chunks/2hr13i-t0krnb.js" 6d:"/_next/static/chunks/0hy4a31wjpabs.js" 6e:"/_next/static/chunks/2mw79vtbi206x.js" 6f:"/_next/static/chunks/253gnmj0-cj17.js" 70:I[593304,["$2c","$2d","$2e","$2f","$30","$31","$46","$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"],"OwnedHydration"] 71:T5f0,Before CART, regression analysis was dominated by parametric methods—primarily linear regression and its extensions. These methods make strong assumptions about the functional form of the relationship between predictors and the target. While powerful when assumptions hold, they often fail when the true relationship is complex, nonlinear, or involves intricate interactions between variables. **The limitations of parametric regression:** 1. **Linearity assumption**: Standard regression assumes a linear (or known nonlinear) relationship 2. **Homoscedasticity**: Constant variance assumption often violated in practice 3. **Interaction specification**: Practitioners must manually specify interaction terms 4. **Nonlinearity handling**: Polynomial terms require knowing the correct degree 5. **Interpretability at scale**: High-dimensional models become difficult to interpret **What CART offered:** CART provided a fundamentally different approach—one that makes no assumptions about functional form at all. Instead of fitting a global model, CART creates a **piecewise model** by recursively partitioning the feature space into regions where simple predictions (constants or linear models) suffice. This approach: - Automatically captures nonlinearity without specification - Discovers interactions through the tree structure - Adapts to local patterns in different regions of the feature space - Produces highly interpretable decision rules - Handles mixed data types (continuous and categorical) naturally72:Tc00,def cart_regression(X, y, max_depth=None, min_samples_split=2, min_samples_leaf=1, current_depth=0): """ CART Regression Algorithm - Core Implementation This function implements the recursive partitioning procedure that forms the heart of the CART regression algorithm. Parameters: ----------- X : array-like, shape (n_samples, n_features) Training feature matrix y : array-like, shape (n_samples,) Target values (continuous) max_depth : int or None Maximum depth of the tree (None for unlimited) min_samples_split : int Minimum samples required to split a node min_samples_leaf : int Minimum samples required in each leaf current_depth : int Current depth in the recursion (internal use) Returns: -------- node : dict A dictionary representing the tree node """ n_samples, n_features = X.shape # BASE CASE 1: Check stopping criteria # ==================================== # These conditions determine when to create a leaf node # Create leaf if max depth reached if max_depth is not None and current_depth >= max_depth: return create_leaf_node(y) # Create leaf if insufficient samples to split if n_samples < min_samples_split: return create_leaf_node(y) # Create leaf if all targets are identical (pure node) if np.var(y) == 0: return create_leaf_node(y) # RECURSIVE CASE: Find best split # ================================ best_split = find_best_split(X, y, min_samples_leaf) # Create leaf if no valid split found if best_split is None: return create_leaf_node(y) feature_idx = best_split['feature_index'] threshold = best_split['threshold'] # Partition samples based on best split left_mask = X[:, feature_idx] <= threshold right_mask = ~left_mask X_left, y_left = X[left_mask], y[left_mask] X_right, y_right = X[right_mask], y[right_mask] # Recursively build left and right subtrees left_child = cart_regression( X_left, y_left, max_depth, min_samples_split, min_samples_leaf, current_depth + 1 ) right_child = cart_regression( X_right, y_right, max_depth, min_samples_split, min_samples_leaf, current_depth + 1 ) # Return internal node return { 'type': 'internal', 'feature_index': feature_idx, 'threshold': threshold, 'left': left_child, 'right': right_child, 'n_samples': n_samples, 'impurity': np.var(y) # MSE before split } def create_leaf_node(y): """ Create a leaf node with constant prediction. For regression, the optimal prediction that minimizes squared error is the mean of target values in the region. """ return { 'type': 'leaf', 'prediction': np.mean(y), 'n_samples': len(y), 'variance': np.var(y), 'std': np.std(y) }73:T469,The heart of CART lies in the `find_best_split` function—the procedure that identifies the single best way to divide a node into two children. For regression, this means finding the split that most reduces prediction error. **The Split Search Problem:** For a node containing $n$ samples with target values $y_1, \ldots, y_n$, we consider splits of the form: $$\text{Split}(j, t): \quad X_j \leq t \quad \text{vs} \quad X_j > t$$ where $j$ indexes the feature and $t$ is the threshold value. This creates two regions: - **Left child**: $R_L = \{i : x_{ij} \leq t\}$ - **Right child**: $R_R = \{i : x_{ij} > t\}$ **The Objective Function:** For regression, CART minimizes the **sum of squared residuals** (or equivalently, weighted variance) after the split: $$\text{Cost}(j, t) = \sum_{i \in R_L} (y_i - \bar{y}_L)^2 + \sum_{i \in R_R} (y_i - \bar{y}_R)^2$$ where $\bar{y}_L = \frac{1}{|R_L|} \sum_{i \in R_L} y_i$ and $\bar{y}_R = \frac{1}{|R_R|} \sum_{i \in R_R} y_i$ This is equivalent to minimizing the weighted sum of child variances: $$\text{Cost}(j, t) = n_L \cdot \text{Var}(y_L) + n_R \cdot \text{Var}(y_R)$$74:T1606,def find_best_split(X, y, min_samples_leaf=1): """ Find the optimal binary split for a regression tree node. This function exhaustively searches all features and all possible split points to find the split that minimizes the weighted sum of child node variances (MSE). Parameters: ----------- X : array-like, shape (n_samples, n_features) Feature matrix for current node y : array-like, shape (n_samples,) Target values for current node min_samples_leaf : int Minimum samples required in each child Returns: -------- best_split : dict or None Dictionary with 'feature_index', 'threshold', and 'gain' Returns None if no valid split exists """ n_samples, n_features = X.shape # Calculate impurity of current node (before split) # This is the variance (MSE relative to mean) current_impurity = np.var(y) * n_samples best_gain = 0.0 best_split = None # Iterate through all features for feature_idx in range(n_features): # Get unique values for this feature, sorted feature_values = X[:, feature_idx] unique_values = np.unique(feature_values) # For efficiency, consider midpoints between consecutive values # This avoids redundant evaluations and handles continuous features if len(unique_values) > 1: thresholds = (unique_values[:-1] + unique_values[1:]) / 2 else: continue # Cannot split on single-valued feature # Evaluate each potential threshold for threshold in thresholds: # Partition samples left_mask = feature_values <= threshold right_mask = ~left_mask n_left = np.sum(left_mask) n_right = np.sum(right_mask) # Check minimum leaf size constraint if n_left < min_samples_leaf or n_right < min_samples_leaf: continue # Calculate child node impurities y_left = y[left_mask] y_right = y[right_mask] left_impurity = np.var(y_left) * n_left if n_left > 0 else 0 right_impurity = np.var(y_right) * n_right if n_right > 0 else 0 # Total impurity after split child_impurity = left_impurity + right_impurity # Impurity reduction (gain) gain = current_impurity - child_impurity # Update best if this split is better if gain > best_gain: best_gain = gain best_split = { 'feature_index': feature_idx, 'threshold': threshold, 'gain': gain, 'n_left': n_left, 'n_right': n_right, 'left_mean': np.mean(y_left), 'right_mean': np.mean(y_right) } return best_split def find_best_split_efficient(X, y, min_samples_leaf=1): """ Optimized split finding using sorted scan technique. This O(n log n) approach sorts each feature once, then uses incremental updates to compute split statistics in O(n) per feature instead of O(n²). """ n_samples, n_features = X.shape current_impurity = np.sum((y - np.mean(y))**2) best_gain = 0.0 best_split = None for feature_idx in range(n_features): # Sort by feature value sorted_indices = np.argsort(X[:, feature_idx]) sorted_y = y[sorted_indices] sorted_x = X[sorted_indices, feature_idx] # Initialize running statistics for incremental updates n_left = 0 sum_left = 0.0 sum_sq_left = 0.0 n_right = n_samples sum_right = np.sum(sorted_y) sum_sq_right = np.sum(sorted_y**2) # Scan through potential split points for i in range(n_samples - 1): # Move sample i from right to left yi = sorted_y[i] n_left += 1 sum_left += yi sum_sq_left += yi**2 n_right -= 1 sum_right -= yi sum_sq_right -= yi**2 # Skip if not at a valid split boundary # (must have distinct feature values) if sorted_x[i] == sorted_x[i + 1]: continue # Check minimum leaf size if n_left < min_samples_leaf or n_right < min_samples_leaf: continue # Compute variance using: Var = E[X²] - E[X]² mean_left = sum_left / n_left mean_right = sum_right / n_right var_left = (sum_sq_left / n_left) - mean_left**2 var_right = (sum_sq_right / n_right) - mean_right**2 # Weighted sum of child variances child_impurity = n_left * var_left + n_right * var_right gain = current_impurity - child_impurity if gain > best_gain: best_gain = gain # Threshold at midpoint between consecutive values threshold = (sorted_x[i] + sorted_x[i + 1]) / 2 best_split = { 'feature_index': feature_idx, 'threshold': threshold, 'gain': gain, 'n_left': n_left, 'n_right': n_right } return best_split75:T5f4,A distinctive feature of CART is its exclusive use of **binary splits**—each internal node has exactly two children. This design choice has profound implications for the algorithm's behavior and efficiency. **Why binary splits?** 1. **Computational tractability**: For a continuous feature, we only need to consider O(n) threshold candidates. Multi-way splits would require evaluating an exponential number of partitions. 2. **Sufficient expressiveness**: Any multi-way split can be represented by a sequence of binary splits. A 3-way split on feature $X$ (e.g., $X < a$, $a \leq X < b$, $X \geq b$) is equivalent to two consecutive binary splits. 3. **Algorithmic elegance**: Binary trees have well-studied properties, efficient traversal algorithms, and natural recursive structure. 4. **Interaction detection**: Binary splits can still capture complex interactions through the tree hierarchy—what a single node cannot express, multiple levels can. **Mathematical perspective:** Let $T$ be the set of all possible trees. Finding the optimal tree is: $$\min_{T} \sum_{m=1}^{|T|} \sum_{i \in R_m} (y_i - \bar{y}_{R_m})^2 + \lambda |T|$$ where $|T|$ is tree complexity and $\lambda$ is a regularization parameter. This optimization is NP-complete because the number of possible trees grows super-exponentially with depth. By restricting to binary splits and using greedy optimization, CART trades global optimality for polynomial-time solvability—a trade-off that has proven remarkably successful in practice.76:Ta2b,def find_best_categorical_split(feature, y, min_samples_leaf=1): """ Find optimal binary split for a categorical feature using the sorted-means optimization for regression. This exploits the fact that for MSE, optimal splits always separate contiguous groups when categories are ordered by mean. """ # Get unique categories and their statistics categories = np.unique(feature) n_categories = len(categories) if n_categories < 2: return None # Compute mean target for each category category_means = {} category_counts = {} for cat in categories: mask = feature == cat category_means[cat] = np.mean(y[mask]) category_counts[cat] = np.sum(mask) # Sort categories by their mean (ascending) sorted_cats = sorted(categories, key=lambda c: category_means[c]) # Now search only over contiguous splits # This is O(k) instead of O(2^k) best_gain = 0.0 best_split = None total_var = np.var(y) * len(y) # Accumulate statistics as we scan through sorted categories n_left = 0 sum_left = 0.0 sum_sq_left = 0.0 n_right = len(y) sum_right = np.sum(y) sum_sq_right = np.sum(y**2) for i in range(n_categories - 1): # Move category sorted_cats[i] from right to left cat = sorted_cats[i] mask = feature == cat cat_y = y[mask] n_cat = len(cat_y) n_left += n_cat sum_left += np.sum(cat_y) sum_sq_left += np.sum(cat_y**2) n_right -= n_cat sum_right -= np.sum(cat_y) sum_sq_right -= np.sum(cat_y**2) # Check minimum leaf size if n_left < min_samples_leaf or n_right < min_samples_leaf: continue # Compute impurity using incremental statistics mean_left = sum_left / n_left mean_right = sum_right / n_right var_left = (sum_sq_left / n_left) - mean_left**2 var_right = (sum_sq_right / n_right) - mean_right**2 child_impurity = n_left * var_left + n_right * var_right gain = total_var - child_impurity if gain > best_gain: best_gain = gain # Left group contains first i+1 categories in sorted order left_categories = set(sorted_cats[:i+1]) best_split = { 'left_categories': left_categories, 'gain': gain, 'n_left': n_left, 'n_right': n_right } return best_split77:Te26,def predict(tree, X): """ Make predictions for multiple samples. Parameters: ----------- tree : dict Root node of the trained CART tree X : array-like, shape (n_samples, n_features) Feature matrix for prediction Returns: -------- predictions : array, shape (n_samples,) Predicted target values """ return np.array([predict_single(tree, x) for x in X]) def predict_single(node, x): """ Traverse tree to predict for a single sample. This recursive function follows the decision path from root to leaf, returning the leaf's prediction. """ # Base case: reached leaf node if node['type'] == 'leaf': return node['prediction'] # Recursive case: follow appropriate branch feature_idx = node['feature_index'] threshold = node['threshold'] if x[feature_idx] <= threshold: return predict_single(node['left'], x) else: return predict_single(node['right'], x) def predict_with_path(node, x, path=None): """ Predict and return the decision path taken. Useful for interpretability and debugging. """ if path is None: path = [] if node['type'] == 'leaf': return node['prediction'], path feature_idx = node['feature_index'] threshold = node['threshold'] if x[feature_idx] <= threshold: path.append({ 'feature': feature_idx, 'threshold': threshold, 'direction': 'left', 'condition': f'X[{feature_idx}] <= {threshold:.4f}' }) return predict_with_path(node['left'], x, path) else: path.append({ 'feature': feature_idx, 'threshold': threshold, 'direction': 'right', 'condition': f'X[{feature_idx}] > {threshold:.4f}' }) return predict_with_path(node['right'], x, path) def get_leaf_regions(tree, bounds=None, feature_names=None): """ Extract all leaf regions with their bounds and predictions. This function recursively traverses the tree and accumulates the constraints that define each region. """ regions = [] _extract_regions(tree, bounds or {}, regions, feature_names) return regions def _extract_regions(node, current_bounds, regions, feature_names): """Helper function to recursively extract regions.""" if node['type'] == 'leaf': regions.append({ 'bounds': current_bounds.copy(), 'prediction': node['prediction'], 'n_samples': node['n_samples'], 'variance': node.get('variance', 0) }) return feature_idx = node['feature_index'] threshold = node['threshold'] fname = feature_names[feature_idx] if feature_names else f'X{feature_idx}' # Left child: add upper bound constraint left_bounds = current_bounds.copy() if fname in left_bounds: left_bounds[fname] = (left_bounds[fname][0], min(left_bounds[fname][1], threshold)) else: left_bounds[fname] = (float('-inf'), threshold) _extract_regions(node['left'], left_bounds, regions, feature_names) # Right child: add lower bound constraint right_bounds = current_bounds.copy() if fname in right_bounds: right_bounds[fname] = (max(right_bounds[fname][0], threshold), right_bounds[fname][1]) else: right_bounds[fname] = (threshold, float('inf')) _extract_regions(node['right'], right_bounds, regions, feature_names)78:Te5b,class CARTRegressionTree: """ Complete CART Regression Tree implementation with all standard stopping criteria. """ def __init__(self, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_impurity_decrease=0.0, max_leaf_nodes=None): """ Initialize CART tree with stopping hyperparameters. Parameters: ----------- max_depth : int or None Maximum tree depth. None for unlimited. min_samples_split : int Minimum samples required to attempt a split. min_samples_leaf : int Minimum samples required in each leaf. min_impurity_decrease : float Minimum impurity reduction to accept a split. A split is only made if: impurity_parent - weighted_impurity_children >= threshold max_leaf_nodes : int or None Maximum number of leaf nodes. If set, grows best-first (highest gain) rather than depth-first. """ self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_impurity_decrease = min_impurity_decrease self.max_leaf_nodes = max_leaf_nodes self.tree_ = None self.n_features_ = None def should_stop(self, n_samples, depth, impurity, best_gain): """ Evaluate all stopping criteria. Returns True if any stopping condition is met. """ # Maximum depth reached if self.max_depth is not None and depth >= self.max_depth: return True # Insufficient samples to split if n_samples < self.min_samples_split: return True # Pure node (zero impurity) if impurity <= 1e-10: return True # No valid split found (best_gain is None) if best_gain is None: return True # Insufficient impurity decrease if best_gain < self.min_impurity_decrease: return True return False def fit(self, X, y): """Fit the regression tree to training data.""" self.n_features_ = X.shape[1] if self.max_leaf_nodes is not None: self.tree_ = self._grow_best_first(X, y) else: self.tree_ = self._grow_depth_first(X, y, depth=0) return self def _grow_depth_first(self, X, y, depth): """Standard depth-first recursive tree growing.""" n_samples = len(y) impurity = np.var(y) * n_samples best_split = find_best_split(X, y, self.min_samples_leaf) best_gain = best_split['gain'] if best_split else None if self.should_stop(n_samples, depth, impurity, best_gain): return create_leaf_node(y) # Create split and recurse feature_idx = best_split['feature_index'] threshold = best_split['threshold'] left_mask = X[:, feature_idx] <= threshold right_mask = ~left_mask return { 'type': 'internal', 'feature_index': feature_idx, 'threshold': threshold, 'left': self._grow_depth_first(X[left_mask], y[left_mask], depth+1), 'right': self._grow_depth_first(X[right_mask], y[right_mask], depth+1), 'n_samples': n_samples, 'impurity': impurity / n_samples }79:T203b,import numpy as np from typing import Optional, Dict, Any, List, Tuple class CARTRegressor: """ Complete CART Regression Tree Implementation This class implements the Classification and Regression Trees (CART) algorithm for regression as described by Breiman et al. (1984). The implementation uses the MSE (variance) splitting criterion and supports all standard hyperparameters for tree construction. Attributes: ----------- tree_ : dict The trained tree structure after calling fit() n_features_ : int Number of features in training data feature_importances_ : ndarray Impurity-based feature importance scores (sum to 1) Examples: --------- >>> tree = CARTRegressor(max_depth=5, min_samples_leaf=10) >>> tree.fit(X_train, y_train) >>> predictions = tree.predict(X_test) >>> print(f"R² Score: {tree.score(X_test, y_test):.4f}") """ def __init__(self, max_depth: Optional[int] = None, min_samples_split: int = 2, min_samples_leaf: int = 1, min_impurity_decrease: float = 0.0): self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_impurity_decrease = min_impurity_decrease # Attributes set during fitting self.tree_: Optional[Dict[str, Any]] = None self.n_features_: Optional[int] = None self.feature_importances_: Optional[np.ndarray] = None self._impurity_gains: List[Tuple[int, float]] = [] def fit(self, X: np.ndarray, y: np.ndarray) -> 'CARTRegressor': """ Build the regression tree from training data. Parameters: ----------- X : ndarray, shape (n_samples, n_features) Training feature matrix y : ndarray, shape (n_samples,) Target values Returns: -------- self : CARTRegressor Fitted estimator """ X = np.atleast_2d(X) y = np.asarray(y).ravel() if len(X) != len(y): raise ValueError("X and y must have same number of samples") self.n_features_ = X.shape[1] self._impurity_gains = [] # Grow tree recursively self.tree_ = self._build_tree(X, y, depth=0) # Compute feature importances from accumulated gains self._compute_feature_importances() return self def _build_tree(self, X: np.ndarray, y: np.ndarray, depth: int) -> Dict[str, Any]: """Recursively build tree structure.""" n_samples = len(y) impurity = np.var(y) # Check stopping criteria if self._should_stop(n_samples, depth, impurity): return self._create_leaf(y) # Find best split best = self._find_best_split(X, y) if best is None or best['gain'] < self.min_impurity_decrease: return self._create_leaf(y) # Record split for feature importance self._impurity_gains.append((best['feature'], best['gain'])) # Partition data left_mask = X[:, best['feature']] <= best['threshold'] right_mask = ~left_mask # Check child size constraints if (np.sum(left_mask) < self.min_samples_leaf or np.sum(right_mask) < self.min_samples_leaf): return self._create_leaf(y) # Build children recursively return { 'type': 'internal', 'feature': best['feature'], 'threshold': best['threshold'], 'left': self._build_tree(X[left_mask], y[left_mask], depth + 1), 'right': self._build_tree(X[right_mask], y[right_mask], depth + 1), 'n_samples': n_samples, 'impurity': impurity, 'gain': best['gain'] } def _should_stop(self, n_samples: int, depth: int, impurity: float) -> bool: """Evaluate stopping conditions.""" if self.max_depth is not None and depth >= self.max_depth: return True if n_samples < self.min_samples_split: return True if impurity < 1e-10: # Pure node return True return False def _create_leaf(self, y: np.ndarray) -> Dict[str, Any]: """Create leaf node with prediction.""" return { 'type': 'leaf', 'prediction': float(np.mean(y)), 'n_samples': len(y), 'variance': float(np.var(y)), 'std': float(np.std(y)) } def _find_best_split(self, X: np.ndarray, y: np.ndarray) -> Optional[Dict[str, Any]]: """Find optimal split using efficient sorted scan.""" n_samples, n_features = X.shape parent_impurity = np.var(y) * n_samples best_gain = 0.0 best_split = None for feat_idx in range(n_features): # Sort by feature order = np.argsort(X[:, feat_idx]) sorted_y = y[order] sorted_x = X[order, feat_idx] # Running statistics n_left, sum_left, sum_sq_left = 0, 0.0, 0.0 n_right = n_samples sum_right = np.sum(sorted_y) sum_sq_right = np.sum(sorted_y ** 2) for i in range(n_samples - 1): yi = sorted_y[i] # Update statistics n_left += 1 sum_left += yi sum_sq_left += yi ** 2 n_right -= 1 sum_right -= yi sum_sq_right -= yi ** 2 # Skip duplicate feature values if sorted_x[i] == sorted_x[i + 1]: continue # Check min_samples_leaf if n_left < self.min_samples_leaf: continue if n_right < self.min_samples_leaf: break # Compute child variances mean_left = sum_left / n_left mean_right = sum_right / n_right var_left = max(0, sum_sq_left/n_left - mean_left**2) var_right = max(0, sum_sq_right/n_right - mean_right**2) child_impurity = n_left * var_left + n_right * var_right gain = parent_impurity - child_impurity if gain > best_gain: best_gain = gain best_split = { 'feature': feat_idx, 'threshold': (sorted_x[i] + sorted_x[i+1]) / 2, 'gain': gain } return best_split def predict(self, X: np.ndarray) -> np.ndarray: """Predict target values for samples.""" X = np.atleast_2d(X) return np.array([self._predict_single(self.tree_, x) for x in X]) def _predict_single(self, node: Dict, x: np.ndarray) -> float: """Traverse tree for single prediction.""" if node['type'] == 'leaf': return node['prediction'] if x[node['feature']] <= node['threshold']: return self._predict_single(node['left'], x) return self._predict_single(node['right'], x) def score(self, X: np.ndarray, y: np.ndarray) -> float: """Compute R² score.""" y_pred = self.predict(X) ss_res = np.sum((y - y_pred) ** 2) ss_tot = np.sum((y - np.mean(y)) ** 2) return 1 - ss_res / ss_tot if ss_tot > 0 else 0.0 def _compute_feature_importances(self): """Compute impurity-based feature importances.""" importances = np.zeros(self.n_features_) for feat_idx, gain in self._impurity_gains: importances[feat_idx] += gain total = importances.sum() if total > 0: importances /= total self.feature_importances_ = importances7a:T5e8,from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import cross_val_score, GridSearchCV import matplotlib.pyplot as plt # Create and train regression tree tree = DecisionTreeRegressor( max_depth=5, min_samples_split=10, min_samples_leaf=5, random_state=42 ) tree.fit(X_train, y_train) # Evaluate performance train_score = tree.score(X_train, y_train) test_score = tree.score(X_test, y_test) print(f"Training R²: {train_score:.4f}") print(f"Test R²: {test_score:.4f}") # Cross-validation for robust performance estimate cv_scores = cross_val_score(tree, X, y, cv=5, scoring='r2') print(f"CV R² Score: {cv_scores.mean():.4f} (+/- {cv_scores.std()*2:.4f})") # Hyperparameter tuning param_grid = { 'max_depth': [3, 5, 7, 10, None], 'min_samples_split': [2, 5, 10, 20], 'min_samples_leaf': [1, 2, 5, 10] } grid_search = GridSearchCV( DecisionTreeRegressor(random_state=42), param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1 ) grid_search.fit(X_train, y_train) print(f"Best parameters: {grid_search.best_params_}") print(f"Best CV MSE: {-grid_search.best_score_:.4f}") # Feature importance analysis importances = tree.feature_importances_ indices = np.argsort(importances)[::-1] plt.figure(figsize=(10, 6)) plt.title("Feature Importances") plt.bar(range(len(importances)), importances[indices]) plt.xticks(range(len(importances)), [feature_names[i] for i in indices], rotation=45) plt.tight_layout() plt.show()45:["$","$L70",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792296755951,"state":{"data":{"json":{"archivedAt":null,"id":"seed_b2b87dff658cc600bb5313282ab109da","slug":"machine-learning","title":"Machine Learning","description":"Learn the mathematical and engineering foundations of machine learning, explore executable visualizers, implement algorithms, and validate dataset releases in repository exercises.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Supervised Learning","Unsupervised Learning","Neural Networks","Deep Learning","Reinforcement Learning","ML Engineering"],"estimatedHours":252.3,"problemCount":39,"visualizerCount":12,"quizCount":0,"learningCount":277,"difficulty":"ADVANCED","idealFor":["Data scientists and ML engineers","Software engineers transitioning to ML","Researchers and academics","Anyone wanting to understand AI/ML deeply"],"prerequisites":["Basic programming (Python preferred)","High school math (algebra, basic calculus)","Comfort with mathematical notation"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithms","Deep learning architectures","Model training and evaluation","Feature engineering","ML system design"],"outcomes":["Build and train ML models from scratch","Understand the math behind algorithms","Apply appropriate algorithms to problems","Design neural network architectures","Deploy and maintain ML systems","Stay current with ML research"],"overview":"This comprehensive learning path takes you from mathematical foundations to cutting-edge ML techniques, preparing you for ML engineering and research roles.\n\n**What you'll learn:**\n• Mathematical foundations (linear algebra, calculus, probability)\n• Statistical learning theory\n• Regression and classification algorithms\n• Ensemble methods (Random Forests, XGBoost)\n• Model evaluation and hyperparameter tuning\n• Unsupervised learning (clustering, dimensionality reduction)\n• Bayesian methods and probabilistic models\n• Neural networks and deep learning\n• CNNs, RNNs, and Transformers\n• Generative models and reinforcement learning\n• ML engineering and deployment\n\nEach topic includes detailed explanations with mathematical rigor.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.124Z","modules":[{"id":"seed_458ccce80e8010395b21b166cb7157a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"What Is Machine Learning?","order":1,"contentType":"LEARNING","contentSlug":"what-is-machine-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_713656282288e703aa91193ff6640a1e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"The Machine Learning Pipeline","order":2,"contentType":"LEARNING","contentSlug":"the-ml-pipeline","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_265702d858afc6a50a187b96538c09bf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Key Concepts and Terminology","order":3,"contentType":"LEARNING","contentSlug":"key-concepts-and-terminology","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3f6d09c97b1e031f237b2abc340e3cbd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Machine Learning Success Factors","order":4,"contentType":"LEARNING","contentSlug":"ml-success-factors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_f1d78b0dba00cdd6d7891c54667b63c0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"What Is Machine Learning: The Foundation of Intelligent Systems","order":1,"contentType":"LEARNING","contentSlug":"what-is-ml","description":null,"estimatedMins":20,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3cd2b6f1422ffede96de1e0597239aa9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"The Machine Learning Landscape","order":2,"contentType":"LEARNING","contentSlug":"ml-landscape","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_be70ea380f07d6ea7e2bdcfc3143d40a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"When to Use Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"when-to-use-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_0160e8621c11b759ae3cc62ac74840f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_62c3a9c331653ca339db3e3f28ff8baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Eigenvalues and Eigenvectors","order":2,"contentType":"LEARNING","contentSlug":"eigenvalues-and-eigenvectors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_79a3d1325d227a51f27b9ef5234cd9a9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Matrix Decompositions","order":3,"contentType":"LEARNING","contentSlug":"matrix-decompositions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1a7e06e15bba9acf14df3d33c9f77125","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Projections and Least Squares","order":4,"contentType":"LEARNING","contentSlug":"projections-and-least-squares","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fe00df3a3ce80f8b8080b9d940134165","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":5,"contentType":"VISUALIZER","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_95423ea3af30d0a2a29f5e934888f366","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":6,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_58253712c8015db94782d197ddc36627","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Pairwise Matrix Product Computation","order":7,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_22c7972151c0c5ecdeb281eb35ce19d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":8,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_83ea72a6e731d4dbcd355861ca589959","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Matrices and Linear Transformations","order":1,"contentType":"LEARNING","contentSlug":"matrices-and-linear-transformations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_e52e478149e70c47f5ecdb61ff144516","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Norms and Distance Metrics","order":2,"contentType":"LEARNING","contentSlug":"norms-and-distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1b167b3e9032b1f5cd4490b4f4ef40b2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":3,"contentType":"VISUALIZER","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_e4f70753ddb0e7daa22b01cddae4d0cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":4,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d267450d674287a1391f487fe97633fa","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Feature Covariance Computation","order":5,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_715f767142a8ca5f68ecbe95188b9171","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_136b74dda11c6ede585436811675d040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Multivariate Calculus","order":2,"contentType":"LEARNING","contentSlug":"multivariate-calculus","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_5d93b2deb9a5171452b8f10b0927266b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Convex Optimization","order":3,"contentType":"LEARNING","contentSlug":"convex-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c17e63b9a5e2e3517a5c1a05e1c8687e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Descent","order":4,"contentType":"LEARNING","contentSlug":"gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_58a6b4d26b778b4a73205b09fd21b0f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Constrained Optimization","order":5,"contentType":"LEARNING","contentSlug":"constrained-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_b0bf23bffa2221135e1bfa0877fc16a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Advanced Optimization Methods","order":6,"contentType":"LEARNING","contentSlug":"advanced-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_87fed16558b28ac02f40835f8205b2eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":7,"contentType":"VISUALIZER","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1e177e352ea702e7bdeb96bc180c96b0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":8,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6538cb1ab6830bbd13c96c2518a5bbab","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Vector Computation for Scalar Fields","order":9,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_73aa88219e07d9fd5f5fb52f60c5bde7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_501a2bc6ca2690e1bb6027ebab947dbb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Random Variables and Distributions","order":2,"contentType":"LEARNING","contentSlug":"random-variables-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_4bedc7e467feee3964d3f6d9adc5c5a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Common Probability Distributions","order":3,"contentType":"LEARNING","contentSlug":"common-probability-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_de316035d6a3b7c5c82a9b8d273802de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Joint and Marginal Distributions","order":4,"contentType":"LEARNING","contentSlug":"joint-marginal-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_3e205a6ba092f97e52401620397afc15","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Estimation Theory","order":5,"contentType":"LEARNING","contentSlug":"estimation-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_9d79f7e518e418caedc3c16ad4567312","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Information Theory for Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"information-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c5e69fdba5ec3502e7ecb9a2729a06be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Population Mean Confidence Estimator","order":7,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_5b652c4511070dff688b90df174e04cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Independent Samples Mean Comparison","order":8,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_66416329d1070e25819caacd6051f101","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Learning Problem","order":1,"contentType":"LEARNING","contentSlug":"the-learning-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_27b627da90e780a7304764b0313cb5a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"PAC Learning: Probably Approximately Correct Framework","order":2,"contentType":"LEARNING","contentSlug":"pac-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_91308bda8e0cece0354a33420f6bab7c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"VC Dimension and Model Complexity","order":3,"contentType":"LEARNING","contentSlug":"vc-dimension","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_74a06a67e984c7f33ea72321e27fe638","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Bias-Variance Tradeoff","order":4,"contentType":"LEARNING","contentSlug":"bias-variance-tradeoff","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6f12c2ff794f5c0a3d28930627f9e9ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Regularization Theory","order":5,"contentType":"LEARNING","contentSlug":"regularization-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_8848fbde5a5ec45b7dcbbb6bcd4ab9ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Generalization Bounds: Mathematical Guarantees for Learning","order":6,"contentType":"LEARNING","contentSlug":"generalization-bounds","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_82fa8bc43938f7a884129594f86d7d81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Regression Diagnostics","order":1,"contentType":"LEARNING","contentSlug":"regression-diagnostics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_c6e739d9eb19caa6b379150681b5e7cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Computational Considerations in Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"computational-considerations-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_829f2ade22abec4e2b345562ce75988e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_f2efa2b99627070158e4ef4b79c540a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Multiple Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"multiple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a7bdfcdeda9f1afd7fb7b9fc202d3f81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Statistical Properties of OLS","order":3,"contentType":"LEARNING","contentSlug":"statistical-properties-of-ols","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_916ec3501e133742dec47a2102539082","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Maximum Likelihood View of Linear Regression","order":4,"contentType":"LEARNING","contentSlug":"maximum-likelihood-view","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_37c09e6af50bedd36be1dc9600378f95","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":5,"contentType":"VISUALIZER","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_1eaf702ff974ccd2ce05bd4f9821f236","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":6,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_ff8d00adbbea598d4881affb39e91b05","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":7,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d97a7c01ffd0b34a093375e7578d029d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"The Overfitting Problem","order":1,"contentType":"LEARNING","contentSlug":"the-overfitting-problem","description":null,"estimatedMins":40,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_12992e935695a3adf10d331bc32c3762","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Ridge Regression (L2 Regularization)","order":2,"contentType":"LEARNING","contentSlug":"ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6bb2f7c3adb3a36845ac25fd5e6983ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Lasso Regression (L1 Regularization)","order":3,"contentType":"LEARNING","contentSlug":"lasso-regression-l1","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_467edb60b91d6f7d7c4cb891f946f6ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Elastic Net Regularization","order":4,"contentType":"LEARNING","contentSlug":"elastic-net","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_01a0334a7189eee091e1369be1d858ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Bayesian Interpretation of Regularization","order":5,"contentType":"LEARNING","contentSlug":"bayesian-interpretation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_539e0c0df2b9d3fc72bfe1ebe1830538","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Advanced Regularization Techniques","order":6,"contentType":"LEARNING","contentSlug":"advanced-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_598afbdc7bc7fa0451b024fbbb08b0a1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Kernel Ridge Regression","order":1,"contentType":"LEARNING","contentSlug":"kernel-ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_723eff8f5cc227d1626cccbd013817f2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Gaussian Processes Introduction","order":2,"contentType":"LEARNING","contentSlug":"gaussian-processes-introduction","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_cab2603b0eff0b9065ccf19343b8f194","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"The Kernel Trick","order":1,"contentType":"LEARNING","contentSlug":"the-kernel-trick","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_bc8bcb824c6863c43427f9eee6e7f874","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Gaussian Process Kernels and Hyperparameter Optimization","order":2,"contentType":"LEARNING","contentSlug":"gp-kernels-and-hyperparameters","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a8ccd12986493122718a2e8f610b8745","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Computational Aspects of Kernels","order":3,"contentType":"LEARNING","contentSlug":"computational-aspects-of-kernels","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6242715024a6f8cd75cba825c4cf2e87","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel Selection and Design","order":4,"contentType":"LEARNING","contentSlug":"kernel-selection-and-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_766bbb1d8379c0966fce86e377d8aa45","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel-Based Support Vector Classifier Training","order":5,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_480cc6f8d716ebf111dd33d18fc8ce41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Polynomial Regression","order":1,"contentType":"LEARNING","contentSlug":"polynomial-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_6f3094d5cfb0e003129f88a934111408","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Spline Regression Methods","order":2,"contentType":"LEARNING","contentSlug":"spline-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fd56c51bbb955a03cd319577e559ca24","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Generalized Additive Models (GAMs)","order":3,"contentType":"LEARNING","contentSlug":"generalized-additive-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_23ca6d0844f4fd9cf359b5e42366acae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Quantile Regression","order":4,"contentType":"LEARNING","contentSlug":"quantile-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_20d9052919ee9f6e5926ffc684095840","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Robust Regression Methods","order":5,"contentType":"LEARNING","contentSlug":"robust-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_d7ab2a7eb1f69de228f455ae070e7934","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Nonparametric Regression","order":6,"contentType":"LEARNING","contentSlug":"nonparametric-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_78a01a27b8d25b02cf4b06660cd62e0f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression And Glms","sectionOrder":14,"title":"Regularized Logistic Regression","order":1,"contentType":"LEARNING","contentSlug":"module-5-regularized-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_fd8976631490d38a493a49d4ea06d921","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Binary Classification Problem","order":1,"contentType":"LEARNING","contentSlug":"binary-classification-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_a84bde3ef9a84b2740b90dbf47496bc4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"The Logistic Regression Model","order":2,"contentType":"LEARNING","contentSlug":"logistic-regression-model","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_7c3373282cd644a5e90d3ce19e000cae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Maximum Likelihood Estimation for Logistic Regression","order":3,"contentType":"LEARNING","contentSlug":"mle-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_177b12207ca60d43846c02b30bdfc4ae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Multi-class Logistic Regression","order":4,"contentType":"LEARNING","contentSlug":"multi-class-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z"},{"id":"seed_700a56229a39f2c5efb792c516b003de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Generalized Linear Models (GLMs)","order":5,"contentType":"LEARNING","contentSlug":"generalized-linear-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5b5ecd2ea6321d0e366b6369851d7e1f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":6,"contentType":"VISUALIZER","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cc24bec346a9c06530806b63a487f8db","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":7,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94cb522600865b629be81feefa09685d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":8,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dceafab2ca6be5ae2808a6312c2333dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Decision Tree Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"decision-tree-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_66f262a333c03eff5f0ef18781c9d3af","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Splitting Criteria","order":2,"contentType":"LEARNING","contentSlug":"splitting-criteria","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_75b193ee8207ca9f7b2d2866489de306","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Growing Algorithms","order":3,"contentType":"LEARNING","contentSlug":"tree-growing-algorithms","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2460c7b303e40cec69e89a72b8b831e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Pruning and Regularization","order":4,"contentType":"LEARNING","contentSlug":"pruning-and-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f4d666cbe20d00df3882c0b2203eb2c8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Regression Trees","order":5,"contentType":"LEARNING","contentSlug":"regression-trees","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_60576ab2af9ebaac4b42cd4cfc97570c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Limitations and Extensions","order":6,"contentType":"LEARNING","contentSlug":"tree-limitations-and-extensions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5ecf05e48f7a0b21063fc124157e3b41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Recursive Classification Tree Builder","order":7,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6694bec14cdf4dec18589122c2ef6ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Maximum Margin Classifier","order":1,"contentType":"LEARNING","contentSlug":"maximum-margin-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_718289c9fa43b9c118e2c33a9b999fc7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Hard Margin SVM","order":2,"contentType":"LEARNING","contentSlug":"hard-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a42c0383f3c3f0b2cfe805c1906ef67e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Soft Margin SVM","order":3,"contentType":"LEARNING","contentSlug":"soft-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0817db7f4e635c79befbd2ec867d4208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Kernel Support Vector Machines","order":4,"contentType":"LEARNING","contentSlug":"kernel-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7f7f1f41aaf42cfff875745539c688c2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"SVM Optimization","order":5,"contentType":"LEARNING","contentSlug":"svm-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9d6a6fae9d4733d75d9d2c0ef2bf6689","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Multi-class Support Vector Machines","order":6,"contentType":"LEARNING","contentSlug":"multi-class-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2db3a376585980bc271d7b13445f187d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Gaussian Naive Bayes","order":1,"contentType":"LEARNING","contentSlug":"gaussian-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ccb1182e88952a8e41eacf6988a8f962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Linear and Quadratic Discriminant Analysis","order":2,"contentType":"LEARNING","contentSlug":"linear-and-quadratic-discriminant-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fbe1f6cf1357ee3876023e84eeebe2e1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Generative vs Discriminative Models","order":1,"contentType":"LEARNING","contentSlug":"generative-vs-discriminative","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1cc60ba5baf88bab74e808a1eb6e245f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Bayes Classifier","order":2,"contentType":"LEARNING","contentSlug":"bayes-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b88cd36a22d2096598e1f8434cad7cbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Naive Bayes Assumption","order":3,"contentType":"LEARNING","contentSlug":"naive-bayes-assumption","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c827e845d309186b4d828114f01bf6ea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Multinomial and Bernoulli Naive Bayes","order":4,"contentType":"LEARNING","contentSlug":"multinomial-bernoulli-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8e0dbdfdcaf7222bdf2b6330b80049eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Algorithm","order":1,"contentType":"LEARNING","contentSlug":"knn-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9a886703f21dde8f994accd7696e3962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Distance Metrics for K-Nearest Neighbors","order":2,"contentType":"LEARNING","contentSlug":"distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_beabe40abd8e31e1a3461563e488e5ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Curse of Dimensionality","order":3,"contentType":"LEARNING","contentSlug":"curse-of-dimensionality","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a3e281d02cea4be01efa2e8de9d21650","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Efficient K-Nearest Neighbors","order":4,"contentType":"LEARNING","contentSlug":"efficient-knn","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_48ce033aa3506c796b6262f9edcf55a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Voronoi Diagrams & Decision Surfaces","order":5,"contentType":"LEARNING","contentSlug":"voronoi-diagrams-and-decision-surfaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4186620863285c1d7b3ce960bbd55ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Locality-Sensitive Hashing (LSH)","order":6,"contentType":"LEARNING","contentSlug":"locality-sensitive-hashing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_02e815b47429e576631e0381220c4966","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Weighted KNN","order":7,"contentType":"LEARNING","contentSlug":"weighted-knn","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_63ff05a505bafec47763bc44bff3a983","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Variants","order":8,"contentType":"LEARNING","contentSlug":"knn-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bafb1e8a6af32317a42e273f5a15de1c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Ensemble Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"ensemble-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_22c2fdccde0ab9e52f64339f364e757d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Random Forests","order":2,"contentType":"LEARNING","contentSlug":"random-forests","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_00a4486bae311423e333adfc96315696","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Bagging for Other Models","order":3,"contentType":"LEARNING","contentSlug":"bagging-for-other-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c51429550b08bde8708ef8c1c162ef1d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Bootstrap Aggregating: Variance Reduction Through Ensemble Averaging","order":1,"contentType":"LEARNING","contentSlug":"bootstrap-aggregating","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_51f41e3368033fc0b10899ff25d5c5ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Feature Importance","order":2,"contentType":"LEARNING","contentSlug":"feature-importance","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5ad2b982504501af216bbc78e377ae2a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Random Forest Variants","order":3,"contentType":"LEARNING","contentSlug":"random-forest-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f03dc46090058acd74b559494ee32f72","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting Intuition","order":1,"contentType":"LEARNING","contentSlug":"boosting-intuition","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_013f75c45c26c5238649f80602c7eea3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"AdaBoost: Adaptive Boosting","order":2,"contentType":"LEARNING","contentSlug":"adaboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_747bf0ebc82590cbd3552b19fb0b76e9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting as Gradient Descent","order":3,"contentType":"LEARNING","contentSlug":"boosting-as-gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_51f106abb54ddee63c6fb37b54ff5b4a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Gradient Boosting","order":4,"contentType":"LEARNING","contentSlug":"gradient-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dc5adc297f1955b0a18ca701a518df00","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Loss Functions for Boosting","order":5,"contentType":"LEARNING","contentSlug":"loss-functions-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8af9b78e83ce53d210e580fa87ed569b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Regularization in Boosting","order":6,"contentType":"LEARNING","contentSlug":"regularization-in-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fb17f19632cfed0944218bf0287a019f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"XGBoost: Extreme Gradient Boosting","order":1,"contentType":"LEARNING","contentSlug":"xgboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f4ed4ac2f15096908a3839a371aea45d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"LightGBM: Light Gradient Boosting Machine","order":2,"contentType":"LEARNING","contentSlug":"lightgbm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2c9e0dfc3b67f3a1c9330f30e269636b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"CatBoost","order":3,"contentType":"LEARNING","contentSlug":"catboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8a969e8156debd6e794f66d4ba1b8f47","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Hyperparameter Tuning for Boosting","order":4,"contentType":"LEARNING","contentSlug":"hyperparameter-tuning-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b3cc7ecb9af183e57d91f1d32acd0ef2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Feature Engineering for Boosting","order":5,"contentType":"LEARNING","contentSlug":"feature-engineering-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0f0a3fcb7bd62041bb7bef139cbd5bdc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Boosting Best Practices","order":6,"contentType":"LEARNING","contentSlug":"boosting-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ac4e81c68db8fe1d9ac89997df18a501","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c5ef7edae79ba6a58e3fd0d44017ac94","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"ROC and Precision-Recall Curves","order":2,"contentType":"LEARNING","contentSlug":"roc-and-precision-recall-curves","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7e80b225bee84e82f8ac145be8ea9c03","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Multi-class Classification Metrics","order":3,"contentType":"LEARNING","contentSlug":"multi-class-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4d46bb09037b0706babb1caba43f8b9d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Regression Metrics","order":4,"contentType":"LEARNING","contentSlug":"regression-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_43ad46d64dbce2d1516e1fb067ceaf9a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Ranking Metrics","order":5,"contentType":"LEARNING","contentSlug":"ranking-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_817f0073a4cafb13f8f380c749e23c80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Custom and Business Metrics","order":6,"contentType":"LEARNING","contentSlug":"custom-and-business-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d59e5e1e4d128d743b25c62154ab3fbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Softmax Probability Distribution","order":7,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_347487ee43f17af456b511a19a40dca7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Holdout Validation","order":1,"contentType":"LEARNING","contentSlug":"holdout-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e0201302cbabe1c8ac59d7eb9fd7b3b5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Stratified and Group Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"stratified-and-group-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5817575eae860da4b31aff4fbbe86bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Bootstrap Methods","order":3,"contentType":"LEARNING","contentSlug":"bootstrap-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0a5077e15e4a5548c820847a82b9c107","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"K-Fold Cross-Validation","order":1,"contentType":"LEARNING","contentSlug":"k-fold-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_80bb08d6076b67b40673dbd30ce30996","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Time Series Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"time-series-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4caea31c12e70b94b3449288ee507316","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Nested Cross-Validation","order":3,"contentType":"LEARNING","contentSlug":"nested-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c39b44e15b86f86fb46fad967dc035f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Data Partitioning with Cross-Validation Splits","order":4,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a7879f98db25ce0269f64ef3f5fe4bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Hyperparameter Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"hyperparameter-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94e0b48f0fb220a06f9766ad9d913f80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Grid Search","order":2,"contentType":"LEARNING","contentSlug":"grid-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b3ebbda137c88b6799101f8804197d6d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Random Search","order":3,"contentType":"LEARNING","contentSlug":"random-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_eb2d1d9b92ee8ca33315169f186c57a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Bayesian Optimization","order":4,"contentType":"LEARNING","contentSlug":"bayesian-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_217594c65573968f0c532e09b11d0155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Multi-Fidelity Optimization","order":5,"contentType":"LEARNING","contentSlug":"multi-fidelity-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cd56aef607f08e69d72c3e7d013c7a26","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Advanced HPO Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-hpo-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dfc3625133e375f2ddb7281b904bc083","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Problem Formulation","order":1,"contentType":"LEARNING","contentSlug":"clustering-problem-formulation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_677d6ef0d11ced3abb40fe90153268f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Clustering","order":2,"contentType":"LEARNING","contentSlug":"k-means-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d3f71756678ae1cb6211450c7d2ca5ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Variants: Beyond Standard K-Means Clustering","order":3,"contentType":"LEARNING","contentSlug":"k-means-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ad45ea7d5ec46859780dfb1e1b634e18","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Hierarchical Clustering","order":4,"contentType":"LEARNING","contentSlug":"hierarchical-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_781388f8e96bee94ce065ff1db0c0064","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Spectral Clustering","order":5,"contentType":"LEARNING","contentSlug":"spectral-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d758ff45163e4d63f66bd744695a183b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Evaluation","order":6,"contentType":"LEARNING","contentSlug":"clustering-evaluation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_14546b73b6439e5fd290e7eac815870c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":7,"contentType":"VISUALIZER","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_11791ef708ec1c800feac411b9252ab8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_68bdb028486489eb91f411607b9412b6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Density Estimation Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"density-estimation-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_64066580a6eb406e58043cabb534f9da","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Kernel Density Estimation Theory","order":2,"contentType":"LEARNING","contentSlug":"kde-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4234b761eb1944905dac7fc3a64bf7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Gaussian Mixture Models","order":3,"contentType":"LEARNING","contentSlug":"gaussian-mixture-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a2eacc9551297ba9d8a48167d5c81781","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"The Expectation-Maximization Algorithm","order":1,"contentType":"LEARNING","contentSlug":"expectation-maximization-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_22d9cdb6f03f9f0e12d8f69cb35600cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Beyond Gaussian Mixtures","order":2,"contentType":"LEARNING","contentSlug":"beyond-gaussian-mixtures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8758cd49b4805ac155a60d016a0a2e38","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Density-Based Clustering","order":3,"contentType":"LEARNING","contentSlug":"density-based-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_983a6d13254cf67c792c3d012aa231b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Dimensionality Reduction Motivation","order":1,"contentType":"LEARNING","contentSlug":"dimensionality-reduction-motivation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_af0b27842aa6a92042251980b9650ad8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Analysis Theory","order":2,"contentType":"LEARNING","contentSlug":"pca-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0ae7eac699c5b7e0ea73d5f865fb72b9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"PCA Variants","order":3,"contentType":"LEARNING","contentSlug":"pca-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d09ca8e44e15e4da659e7b542cc04446","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Kernel PCA","order":4,"contentType":"LEARNING","contentSlug":"kernel-pca","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_85afe3c0f35cb0210def66038cb352a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Factor Analysis","order":5,"contentType":"LEARNING","contentSlug":"factor-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ab73773fa16c6ed214ba79504d07c444","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Independent Component Analysis","order":6,"contentType":"LEARNING","contentSlug":"independent-component-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c61ed49082d6e5a2589810425c9b31f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":7,"contentType":"VISUALIZER","contentSlug":"principal-component-extraction","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_01b5934d7f8ef0bf58ff57e63421b50f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":8,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1c389cf030159b7da28f11969faaa3b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Manifold Hypothesis","order":1,"contentType":"LEARNING","contentSlug":"manifold-hypothesis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c1c7cd66ec0fed623f6f8f579b7062f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Multidimensional Scaling","order":2,"contentType":"LEARNING","contentSlug":"multidimensional-scaling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_71ecb495764198e1daba7a26711139fc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Isomap: Isometric Feature Mapping","order":3,"contentType":"LEARNING","contentSlug":"isomap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_73855f5207a19a961f18fcd49f63f1a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Locally Linear Embedding","order":4,"contentType":"LEARNING","contentSlug":"locally-linear-embedding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_492a583a08bf6d335d7e2a481be94363","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"t-Distributed Stochastic Neighbor Embedding (t-SNE)","order":5,"contentType":"LEARNING","contentSlug":"t-sne","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_605375d1d6334b96f0eead774cc314f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"UMAP (Uniform Manifold Approximation and Projection)","order":6,"contentType":"LEARNING","contentSlug":"umap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bff5e7a0bc4fddf9a78ccd4982e6832d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Anomaly Detection Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"anomaly-detection-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a2dd0d8bf5ebd6be590cd9d205ca3b5a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Distance-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"distance-based-methods","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1a47f9a533ee25611d5c267af621d678","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Isolation Forest for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"isolation-forest","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76b018ba7d1f8fc744e0978c016eb04e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Statistical Methods for Anomaly Detection","order":1,"contentType":"LEARNING","contentSlug":"statistical-methods-anomaly-detection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a8fc19bd78ba2746be4fd62a27ece203","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Density-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"density-based-methods-anomaly","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4555f92571404920aa7a0ff10edd4942","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"One-Class Methods for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"one-class-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ef80465133d1fe9ce84bf4ab98e19dea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"The Bayesian Framework","order":1,"contentType":"LEARNING","contentSlug":"bayesian-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e20205c549adb1a21e00c02f2724d38b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Conjugate Priors","order":2,"contentType":"LEARNING","contentSlug":"conjugate-priors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b347cdec69ff8713db8903d02f3d40cd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Linear Regression","order":3,"contentType":"LEARNING","contentSlug":"bayesian-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_017d50d5c031b5e4d0e1445faf6ce982","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Model Comparison","order":4,"contentType":"LEARNING","contentSlug":"bayesian-model-comparison","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d310f58ff165b49c4ec608aa0227cacf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Sampling Methods","order":5,"contentType":"LEARNING","contentSlug":"approximate-inference-sampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5c847f1e908149fa7ac07f86188b319b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Deterministic Methods","order":6,"contentType":"LEARNING","contentSlug":"approximate-inference-deterministic","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_46d5fd4b152d1cd30b9443131d4b0c73","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Posterior Parameter Estimation with Conjugate Prior","order":7,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ca2a8840c6015e1ebbb86e35dbba1513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Probabilistic Graphical Models","order":1,"contentType":"LEARNING","contentSlug":"probabilistic-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f736480433bf6703491b1838f1c22be3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Bayesian Networks","order":2,"contentType":"LEARNING","contentSlug":"bayesian-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_29f3e6fc90cbda45c1970a76aa59d663","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Markov Random Fields","order":3,"contentType":"LEARNING","contentSlug":"markov-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_24b1625575d59d42572164bfa2537fd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Inference in Graphical Models","order":4,"contentType":"LEARNING","contentSlug":"inference-in-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_26906c87473209c81f340c709f82c90c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Hidden Markov Models","order":5,"contentType":"LEARNING","contentSlug":"hidden-markov-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_70298c6e59e6d4aa717b493f4727004f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Conditional Random Fields","order":6,"contentType":"LEARNING","contentSlug":"conditional-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bfcd86cd712852ba44877065af04967f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"gp-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_379822a695955a78bd4ea16f38131ea2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Regression","order":2,"contentType":"LEARNING","contentSlug":"gp-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_9c7afac43216ccc7ff3ab116639b4514","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Kernel Design for Gaussian Processes","order":3,"contentType":"LEARNING","contentSlug":"kernel-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f84c8f27bdd419c923e2b1700bde6637","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Hyperparameter Learning in Gaussian Processes","order":4,"contentType":"LEARNING","contentSlug":"gp-hyperparameter-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6b381cf713c81aa18f4233121bab820e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Scalable Gaussian Processes","order":5,"contentType":"LEARNING","contentSlug":"scalable-gps","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f98eb34b8b26e060c9c2ee26e2db04e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Classification","order":6,"contentType":"LEARNING","contentSlug":"gp-classification","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_848b6363ea4fbdd529dd3bd0015d5968","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Inference Framework","order":1,"contentType":"LEARNING","contentSlug":"variational-inference-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_347f0f34f42595c07ce415157b687b80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Mean-Field Approximation","order":2,"contentType":"LEARNING","contentSlug":"mean-field-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e21603eecdb3d61c60b1ccc6bbea7857","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"ELBO Optimization","order":3,"contentType":"LEARNING","contentSlug":"elbo-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_546b35e4882116c3d0a51680f2d835be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Stochastic Variational Inference","order":4,"contentType":"LEARNING","contentSlug":"stochastic-variational-inference","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1463afd50f358c4c3ce2e904178881a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Autoencoders","order":5,"contentType":"LEARNING","contentSlug":"ml-variational-inference-variational-autoencoders","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e9b79bdc20ae45b33f8b2f486ee6c680","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Advanced VI Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-vi-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f82ea691ae02c2164db9a4a902cfee61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_763ec03462b28e436c290ff5a6028cdd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Multi-Layer Perceptrons","order":2,"contentType":"LEARNING","contentSlug":"multi-layer-perceptrons","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a798e45c47f2ce91b6ef5d8994a2778d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Universal Approximation","order":3,"contentType":"LEARNING","contentSlug":"universal-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_06736591bc987a4d2945f9be6f8633ce","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Functions","order":4,"contentType":"LEARNING","contentSlug":"activation-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ef1c8026b6efc42f60f2b27ef58f6ed5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Loss Functions","order":5,"contentType":"LEARNING","contentSlug":"loss-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6cc64eb0e0ba1c8951cbe4fed8d38c06","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Output Layers","order":6,"contentType":"LEARNING","contentSlug":"output-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dd77b6b05260cf5c413474e5d189ece4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":7,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0d1e9a5fd7d14cd9c1f49cb2ce5b143b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":8,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_81b4675ff4c64769f9e3ca3a0e12a3dc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Backpropagation Algorithm","order":1,"contentType":"LEARNING","contentSlug":"backpropagation-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6f6f054ba88424be3541f892015658c4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Gradient Descent Variants","order":2,"contentType":"LEARNING","contentSlug":"gradient-descent-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0d04bb8be0289593e83fa5d0b2ad1b85","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Adaptive Optimizers","order":3,"contentType":"LEARNING","contentSlug":"adaptive-optimizers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_790d19157f66aa34b92db31dd4a5173b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Learning Rate Schedules: Orchestrating Optimization Dynamics","order":4,"contentType":"LEARNING","contentSlug":"learning-rate-schedules","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_62fc9f7677c99703f946f7592b4dd208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Computational Graphs","order":1,"contentType":"LEARNING","contentSlug":"computational-graphs","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b49f7466079a1b6662ac948c9e560dae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Second-Order Optimization Methods","order":2,"contentType":"LEARNING","contentSlug":"second-order-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_c856bc9ccfc814ca27e37c25317189eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":3,"contentType":"VISUALIZER","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5e38cad8f738a9edc45cd7bb25048d92","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":4,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_79363c50d7cc2df59cf264620abf9d14","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Gradient Computation Engine (Basic Autograd)","order":5,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1a37ecaa3606e5645687fde6f75686e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Capacity and Generalization","order":1,"contentType":"LEARNING","contentSlug":"capacity-and-generalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4556ac961fca197a64db759fdfd37ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Weight Regularization","order":2,"contentType":"LEARNING","contentSlug":"weight-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_28b239ae9dfb30b8bddecfbaa3928c28","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Dropout","order":3,"contentType":"LEARNING","contentSlug":"dropout","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e44f2f1dee2954db26113a899c23bb10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Batch Normalization","order":4,"contentType":"LEARNING","contentSlug":"batch-normalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_09158c8adb4d1dbd43a885617bdbd7b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Data Augmentation: Expanding Training Distributions","order":5,"contentType":"LEARNING","contentSlug":"data-augmentation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_020e4e8a002e13bfe417d1aac7d02803","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Implicit Regularization in Deep Learning","order":6,"contentType":"LEARNING","contentSlug":"implicit-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6851d7814e8e4b64902dfba5a609d2de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_94702d5823572341bdb7c6dac7a81e0d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Convolutional Layers","order":2,"contentType":"LEARNING","contentSlug":"convolutional-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4af0cc0135bdb4df0247addfc29fe2ee","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Pooling and Downsampling","order":3,"contentType":"LEARNING","contentSlug":"pooling-and-downsampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cd8e784eebe78b202a0a8c12e492d917","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"CNN Architectures: From LeNet to Modern Designs","order":4,"contentType":"LEARNING","contentSlug":"cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3437e70f0dc0731e390eaacb69b9232e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Residual Networks and Skip Connections","order":5,"contentType":"LEARNING","contentSlug":"residual-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_308ce9bd101b8dd160bbae4d0fb7454d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Modern CNN Architectures","order":6,"contentType":"LEARNING","contentSlug":"modern-cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cbee09a0e607620cd98f7ddd2e9fa3d1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":7,"contentType":"VISUALIZER","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_110330320fbff6e7329228ba2e790938","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":8,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5f1f573d9929ca2f3046a5cc8bcc8aca","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5db2b8211deeb14b772472b6b9cf59a0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Recurrent Neural Network Architecture","order":2,"contentType":"LEARNING","contentSlug":"rnn-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_162d876b2b2a4f4479fbf1d37a5e4a64","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Vanishing and Exploding Gradients","order":3,"contentType":"LEARNING","contentSlug":"vanishing-exploding-gradients","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3820671dcdd4232fc4cdd4d868cea6d2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Long Short-Term Memory","order":4,"contentType":"LEARNING","contentSlug":"long-short-term-memory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fa7c5556299f5f97c15a403a7b11cb8c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Gated Recurrent Units","order":5,"contentType":"LEARNING","contentSlug":"gated-recurrent-units","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_37f61dfc75c977d4f49c5d259f50879f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Advanced RNN Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-rnn-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_78f2403d64ba60eab0edef165e79785d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":7,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e56a4e18774f4156d0a77d0180048778","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":8,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6a5518e35a9be244d03d87d3d56f50e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e9ba71f8e6d0d3c6af728ccfb81965d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention","order":2,"contentType":"LEARNING","contentSlug":"self-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2e1937ca8c0da75933b343ae37dc2cd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Multi-Head Attention","order":3,"contentType":"LEARNING","contentSlug":"multi-head-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_042e91151586cd6a2bb93b767eee318d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Architecture","order":4,"contentType":"LEARNING","contentSlug":"transformer-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_79661700b7c47fae120d0bd30aa077f8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Variants","order":5,"contentType":"LEARNING","contentSlug":"transformer-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2c16c77d2922828776983c6932e17364","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":6,"contentType":"VISUALIZER","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6ea15e630262800d691de2dbdb9277ad","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":7,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ea4826b8cb5aa5c6c91df6bbd5b3562f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention Transformers","sectionOrder":47,"title":"Positional Encoding","order":1,"contentType":"LEARNING","contentSlug":"positional-encoding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8a58a3ebe0e519a060e593bb2f787e77","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_04aa20659bf19165d9aef4e2b23a6ff7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Adversarial Networks","order":2,"contentType":"LEARNING","contentSlug":"generative-adversarial-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_fbcfefe6c406993e3296659d69308542","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"GAN Variants","order":3,"contentType":"LEARNING","contentSlug":"gan-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_b7c820446f87f68f23f8ec05b9981baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Flow-Based Models","order":4,"contentType":"LEARNING","contentSlug":"flow-based-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ec6ae3647c56081215e3cbf9ded2e8dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Diffusion Models","order":5,"contentType":"LEARNING","contentSlug":"diffusion-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_904927660fd8d6c41d1aba5f40bf3672","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised And Self Supervised Learning","sectionOrder":49,"title":"The Label Scarcity Problem","order":1,"contentType":"LEARNING","contentSlug":"the-label-scarcity-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e4208222d89c41fea5968e9e5f3c53d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Semi-Supervised Methods","order":1,"contentType":"LEARNING","contentSlug":"semi-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3bff5f8dfe463704b7b4b20e9fa5d91c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Consistency Regularization","order":2,"contentType":"LEARNING","contentSlug":"consistency-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cef5c1d33e1344056ba5a145591e865b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Self-Supervised Learning","order":3,"contentType":"LEARNING","contentSlug":"self-supervised-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_28431ac2324f528d7232189a00afdae7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Contrastive Learning","order":4,"contentType":"LEARNING","contentSlug":"contrastive-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_89bfe981eb096f5677324a5cb95c5c3f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Modern Self-Supervised Methods","order":5,"contentType":"LEARNING","contentSlug":"modern-self-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_48b71cced9af84fce00dd5f87802a040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning And Domain Adaptation","sectionOrder":51,"title":"Multi-Task Learning","order":1,"contentType":"LEARNING","contentSlug":"multi-task-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_bad0d98a933a15ea25cabed00cac2414","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Transfer Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"transfer-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_18d69040c7cd9a295f5552d746a8012b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Feature-Based Transfer","order":2,"contentType":"LEARNING","contentSlug":"feature-based-transfer","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8e9f01590c85745935d1f7e223be6927","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Fine-Tuning Strategies","order":3,"contentType":"LEARNING","contentSlug":"fine-tuning-strategies","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4c02fca3ba18a5932d556e4a7780eeeb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Domain Adaptation","order":4,"contentType":"LEARNING","contentSlug":"domain-adaptation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3d3815ce091181ea37c5c2491dc123c9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Meta-Learning: Learning to Learn","order":5,"contentType":"LEARNING","contentSlug":"meta-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_146bb4046d37075972f5f1d4a35d8133","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4a14d44ad08d752faaf01a1fe34edd4b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Markov Decision Processes","order":2,"contentType":"LEARNING","contentSlug":"markov-decision-processes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e962e18f072550c1bddf2875fc11d4d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Value-Based Methods","order":3,"contentType":"LEARNING","contentSlug":"value-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8dafdc1a94769c8c45e42c0a06ed363b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Policy-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"policy-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_306e6c5664e5b5241b8ca0658b77ed61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Deep Reinforcement Learning","order":5,"contentType":"LEARNING","contentSlug":"deep-reinforcement-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_83233c3c4eba3d8393e4b5df2a901c2f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Applications and Challenges","order":6,"contentType":"LEARNING","contentSlug":"rl-applications-and-challenges","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_538da3bc0ccbd37d3fab29f08eb07443","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Intra-Group Advantage Normalization for Policy Training","order":7,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f11572f4df3cd111b29c1115c8d584f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":8,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8ab2ad29bcfe76d03a61e3fdfc68781b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7b67e7eda540fa724d88ee493898b69c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Collaborative Filtering","order":2,"contentType":"LEARNING","contentSlug":"collaborative-filtering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_8b451bfeded3345408e779b5bc2b7fb1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Matrix Factorization for Recommendations","order":3,"contentType":"LEARNING","contentSlug":"matrix-factorization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_19703d4984d6329646468830d58dec7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Content-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"content-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2ab9a2c09cd93ba87112ecc170e53513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Deep Learning for Recommendations","order":5,"contentType":"LEARNING","contentSlug":"deep-learning-for-recommendations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2af5ea0db1dad6b70b5104436968ca34","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Production Considerations","order":6,"contentType":"LEARNING","contentSlug":"production-considerations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_388dd9ab336383cfe8b60203b6a21a39","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_5de18599b9e5181b808780cb9c304da9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Selection Theory","order":2,"contentType":"LEARNING","contentSlug":"feature-selection-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_497f3f5dbb61fb7014f109d9f0eb336b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Text Feature Engineering: N-grams","order":3,"contentType":"LEARNING","contentSlug":"text-feature-engineering-ngrams","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7f90aa61bc4fb373dafbad502a5f0fd6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":4,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_741941446f08f63cd865ccae73a1a71f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Categorical Binary Encoding","order":5,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7df14192fe8a798ae765e3b012cede59","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Ordered Polynomial Feature Generator","order":6,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_842f5163e5a439bbac1ef1918b42633a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_81d80261d64ffea6e4533de78d2b9dd0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Stores: The Foundation of Production ML Feature Management","order":2,"contentType":"LEARNING","contentSlug":"feature-stores","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f944d98b4893ec36ff4f28076308eeb2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Transformation Pipelines","order":3,"contentType":"LEARNING","contentSlug":"feature-transformation-pipelines","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cc3d8ae2e097a16d9fcc802ad211391b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"High-Cardinality Features","order":4,"contentType":"LEARNING","contentSlug":"high-cardinality-features","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a27695ebceaabefd2a3d7ab9d65fb829","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering - Basics","order":5,"contentType":"LEARNING","contentSlug":"text-feature-engineering-basics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1c77cb034b5085130e3be375725cf1a3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering: TF-IDF","order":6,"contentType":"LEARNING","contentSlug":"text-feature-engineering-tfidf","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7491c6ce5e01ba00b2b823909fe0d32a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Preprocessing: Transforming Raw Text for Machine Learning","order":7,"contentType":"LEARNING","contentSlug":"text-preprocessing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_7980b68a9f4f240de52a46472d229990","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Word Embeddings","order":8,"contentType":"LEARNING","contentSlug":"word-embeddings","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a9d7121a5834d039ddce6eec079e2e25","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Overview","order":1,"contentType":"LEARNING","contentSlug":"automl-overview","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4822c3e211b027bbc0b9e39d013cd2bd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Preprocessing","order":2,"contentType":"LEARNING","contentSlug":"automated-preprocessing","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_ae082beb489cc334aefa7f1daded438d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Model Selection","order":3,"contentType":"LEARNING","contentSlug":"automated-model-selection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e1b7d2e67067c90c33a877fa1c26d3e6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Neural Architecture Search","order":4,"contentType":"LEARNING","contentSlug":"neural-architecture-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_47b36d588fcad669b42eea822d760f0a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Systems","order":5,"contentType":"LEARNING","contentSlug":"automl-systems","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_cb9a45dc1c54e73311b24cd928b3ab69","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Best Practices","order":6,"contentType":"LEARNING","contentSlug":"automl-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_1458bdc7f12dad04db575fa4a57fe870","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability And Fairness","sectionOrder":58,"title":"Feature Attribution Methods","order":1,"contentType":"LEARNING","contentSlug":"feature-attribution-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0f88251e409f204b5dcec58e017412d4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Interpretability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"interpretability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_09729918b026512bb25f63a10b88afa7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Model-Specific Interpretability","order":2,"contentType":"LEARNING","contentSlug":"model-specific-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6377599b4a10d0e02c1cfc2dc7aaf743","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Fairness in Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"fairness-in-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_4951de29801ee8a44a8759a1597460f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Bias Detection and Mitigation","order":4,"contentType":"LEARNING","contentSlug":"bias-detection-mitigation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_2a221429a71db5df8da0275ce4697a10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Practical Interpretability","order":5,"contentType":"LEARNING","contentSlug":"practical-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_a4064079bf9971ca27d16886141b8871","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Foundation Models","order":1,"contentType":"LEARNING","contentSlug":"foundation-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_50bdd845bc425620c7bd09e44d4f5827","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Large Language Models","order":2,"contentType":"LEARNING","contentSlug":"large-language-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_db062e2abf514009df75d477ba2962e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Multimodal Learning: Bridging Vision, Language, and Beyond","order":3,"contentType":"LEARNING","contentSlug":"multimodal-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_e885aaae2eb8f75705604e9d2293f2ec","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Federated Learning","order":4,"contentType":"LEARNING","contentSlug":"federated-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f6da44328600f666e7d9a74d1c32b432","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Continual Learning: Learning Without Forgetting","order":5,"contentType":"LEARNING","contentSlug":"continual-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_3efeb1d46a6097c7b10ddb9525c4b581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Emerging Directions in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"emerging-directions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_723cca038a2c40f84476ae0e31ab18e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_dbfa0d7a5ff41a228873f58339420ef9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Debugging ML Models","order":2,"contentType":"LEARNING","contentSlug":"debugging-ml-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_29daeedf46dc1d31c9070e587276c581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Project Management","order":3,"contentType":"LEARNING","contentSlug":"ml-project-management","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_800ab48cf1731556a258ea5ec18b78f6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Reading Research Papers","order":4,"contentType":"LEARNING","contentSlug":"reading-research-papers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_f292b4c2034d13213b3e7c675b18f765","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Interviews: Complete Preparation Guide","order":5,"contentType":"LEARNING","contentSlug":"ml-interviews","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_632dc2189cf690119e3942c3dfa69d8b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Career Development in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"career-development","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_39cac80dede8b891307a5865480e576b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Dataset Batch Generator for Training Pipelines","order":7,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76bec3396a0c10a07b961c0659f31f5b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Performance Metrics Calculator","order":8,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_d8bc35e4e84cf3a27ee560839206c155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Request Batching","order":9,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_07789f50da99325787e488c27c2f335e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Synchronized Data Permutation","order":1,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_0818b627401c3528be0e63d2b248925b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"One bad dataset row consumes several quality allowances","order":2,"contentType":"REPO","contentSlug":"dataset-gate-failure-budget","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_76aa74612887d6b05ec63a158ebb9b68","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Count bad dataset rows once","order":3,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-failure-budget","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_82746decf240c4d206937e214136ffe6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Published datasets differ from the snapshot that passed review","order":4,"contentType":"REPO","contentSlug":"dataset-gate-unchecked-publication","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"},{"id":"seed_6a6c35120fbb3874411dee3e7217348d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Publish the rows captured by the quality run","order":5,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-unchecked-publication","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z"}],"sections":[{"title":"Chapter 1: Introduction To Machine Learning","order":1,"modules":["$45:props:state:queries:0:state:data:json:modules:0","$45:props:state:queries:0:state:data:json:modules:1","$45:props:state:queries:0:state:data:json:modules:2","$45:props:state:queries:0:state:data:json:modules:3"]},{"title":"Chapter 1: Introduction To Ml","order":2,"modules":["$45:props:state:queries:0:state:data:json:modules:4","$45:props:state:queries:0:state:data:json:modules:5","$45:props:state:queries:0:state:data:json:modules:6"]},{"title":"Chapter 2: Linear Algebra","order":3,"modules":["$45:props:state:queries:0:state:data:json:modules:7","$45:props:state:queries:0:state:data:json:modules:8","$45:props:state:queries:0:state:data:json:modules:9","$45:props:state:queries:0:state:data:json:modules:10","$45:props:state:queries:0:state:data:json:modules:11","$45:props:state:queries:0:state:data:json:modules:12","$45:props:state:queries:0:state:data:json:modules:13","$45:props:state:queries:0:state:data:json:modules:14"]},{"title":"Chapter 2: Mathematical Foundations Linear Algebra","order":4,"modules":["$45:props:state:queries:0:state:data:json:modules:15","$45:props:state:queries:0:state:data:json:modules:16","$45:props:state:queries:0:state:data:json:modules:17","$45:props:state:queries:0:state:data:json:modules:18","$45:props:state:queries:0:state:data:json:modules:19"]},{"title":"Chapter 3: Calculus Optimization","order":5,"modules":["$45:props:state:queries:0:state:data:json:modules:20","$45:props:state:queries:0:state:data:json:modules:21","$45:props:state:queries:0:state:data:json:modules:22","$45:props:state:queries:0:state:data:json:modules:23","$45:props:state:queries:0:state:data:json:modules:24","$45:props:state:queries:0:state:data:json:modules:25","$45:props:state:queries:0:state:data:json:modules:26","$45:props:state:queries:0:state:data:json:modules:27","$45:props:state:queries:0:state:data:json:modules:28"]},{"title":"Chapter 4: Probability Theory","order":6,"modules":["$45:props:state:queries:0:state:data:json:modules:29","$45:props:state:queries:0:state:data:json:modules:30","$45:props:state:queries:0:state:data:json:modules:31","$45:props:state:queries:0:state:data:json:modules:32","$45:props:state:queries:0:state:data:json:modules:33","$45:props:state:queries:0:state:data:json:modules:34","$45:props:state:queries:0:state:data:json:modules:35","$45:props:state:queries:0:state:data:json:modules:36"]},{"title":"Chapter 5: Statistical Learning Theory","order":7,"modules":["$45:props:state:queries:0:state:data:json:modules:37","$45:props:state:queries:0:state:data:json:modules:38","$45:props:state:queries:0:state:data:json:modules:39","$45:props:state:queries:0:state:data:json:modules:40","$45:props:state:queries:0:state:data:json:modules:41","$45:props:state:queries:0:state:data:json:modules:42"]},{"title":"Chapter 6: Linear Regression","order":8,"modules":["$45:props:state:queries:0:state:data:json:modules:43","$45:props:state:queries:0:state:data:json:modules:44"]},{"title":"Chapter 6: Linear Regression Deep Dive","order":9,"modules":["$45:props:state:queries:0:state:data:json:modules:45","$45:props:state:queries:0:state:data:json:modules:46","$45:props:state:queries:0:state:data:json:modules:47","$45:props:state:queries:0:state:data:json:modules:48","$45:props:state:queries:0:state:data:json:modules:49","$45:props:state:queries:0:state:data:json:modules:50","$45:props:state:queries:0:state:data:json:modules:51"]},{"title":"Chapter 7: Regularization Theory","order":10,"modules":["$45:props:state:queries:0:state:data:json:modules:52","$45:props:state:queries:0:state:data:json:modules:53","$45:props:state:queries:0:state:data:json:modules:54","$45:props:state:queries:0:state:data:json:modules:55","$45:props:state:queries:0:state:data:json:modules:56","$45:props:state:queries:0:state:data:json:modules:57"]},{"title":"Chapter 8: Kernel Methods","order":11,"modules":["$45:props:state:queries:0:state:data:json:modules:58","$45:props:state:queries:0:state:data:json:modules:59"]},{"title":"Chapter 8: Kernel Methods For Regression","order":12,"modules":["$45:props:state:queries:0:state:data:json:modules:60","$45:props:state:queries:0:state:data:json:modules:61","$45:props:state:queries:0:state:data:json:modules:62","$45:props:state:queries:0:state:data:json:modules:63","$45:props:state:queries:0:state:data:json:modules:64"]},{"title":"Chapter 9: Advanced Regression","order":13,"modules":["$45:props:state:queries:0:state:data:json:modules:65","$45:props:state:queries:0:state:data:json:modules:66","$45:props:state:queries:0:state:data:json:modules:67","$45:props:state:queries:0:state:data:json:modules:68","$45:props:state:queries:0:state:data:json:modules:69","$45:props:state:queries:0:state:data:json:modules:70"]},{"title":"Chapter 10: Logistic Regression And Glms","order":14,"modules":["$45:props:state:queries:0:state:data:json:modules:71"]},{"title":"Chapter 10: Logistic Regression Glms","order":15,"modules":["$45:props:state:queries:0:state:data:json:modules:72","$45:props:state:queries:0:state:data:json:modules:73","$45:props:state:queries:0:state:data:json:modules:74","$45:props:state:queries:0:state:data:json:modules:75","$45:props:state:queries:0:state:data:json:modules:76","$45:props:state:queries:0:state:data:json:modules:77","$45:props:state:queries:0:state:data:json:modules:78","$45:props:state:queries:0:state:data:json:modules:79"]},{"title":"Chapter 11: Decision Trees","order":16,"modules":["$45:props:state:queries:0:state:data:json:modules:80","$45:props:state:queries:0:state:data:json:modules:81","$45:props:state:queries:0:state:data:json:modules:82","$45:props:state:queries:0:state:data:json:modules:83","$45:props:state:queries:0:state:data:json:modules:84","$45:props:state:queries:0:state:data:json:modules:85","$45:props:state:queries:0:state:data:json:modules:86"]},{"title":"Chapter 12: Support Vector Machines","order":17,"modules":["$45:props:state:queries:0:state:data:json:modules:87","$45:props:state:queries:0:state:data:json:modules:88","$45:props:state:queries:0:state:data:json:modules:89","$45:props:state:queries:0:state:data:json:modules:90","$45:props:state:queries:0:state:data:json:modules:91","$45:props:state:queries:0:state:data:json:modules:92"]},{"title":"Chapter 13: Naive Bayes And Probabilistic Classifiers","order":18,"modules":["$45:props:state:queries:0:state:data:json:modules:93","$45:props:state:queries:0:state:data:json:modules:94"]},{"title":"Chapter 13: Naive Bayes Probabilistic Classifiers","order":19,"modules":["$45:props:state:queries:0:state:data:json:modules:95","$45:props:state:queries:0:state:data:json:modules:96","$45:props:state:queries:0:state:data:json:modules:97","$45:props:state:queries:0:state:data:json:modules:98"]},{"title":"Chapter 14: K Nearest Neighbors","order":20,"modules":["$45:props:state:queries:0:state:data:json:modules:99","$45:props:state:queries:0:state:data:json:modules:100","$45:props:state:queries:0:state:data:json:modules:101","$45:props:state:queries:0:state:data:json:modules:102","$45:props:state:queries:0:state:data:json:modules:103","$45:props:state:queries:0:state:data:json:modules:104","$45:props:state:queries:0:state:data:json:modules:105","$45:props:state:queries:0:state:data:json:modules:106"]},{"title":"Chapter 15: Bagging And Random Forests","order":21,"modules":["$45:props:state:queries:0:state:data:json:modules:107","$45:props:state:queries:0:state:data:json:modules:108","$45:props:state:queries:0:state:data:json:modules:109"]},{"title":"Chapter 15: Bagging Random Forests","order":22,"modules":["$45:props:state:queries:0:state:data:json:modules:110","$45:props:state:queries:0:state:data:json:modules:111","$45:props:state:queries:0:state:data:json:modules:112"]},{"title":"Chapter 16: Boosting Theory","order":23,"modules":["$45:props:state:queries:0:state:data:json:modules:113","$45:props:state:queries:0:state:data:json:modules:114","$45:props:state:queries:0:state:data:json:modules:115","$45:props:state:queries:0:state:data:json:modules:116","$45:props:state:queries:0:state:data:json:modules:117","$45:props:state:queries:0:state:data:json:modules:118"]},{"title":"Chapter 17: Modern Boosting","order":24,"modules":["$45:props:state:queries:0:state:data:json:modules:119","$45:props:state:queries:0:state:data:json:modules:120","$45:props:state:queries:0:state:data:json:modules:121","$45:props:state:queries:0:state:data:json:modules:122","$45:props:state:queries:0:state:data:json:modules:123","$45:props:state:queries:0:state:data:json:modules:124"]},{"title":"Chapter 18: Model Evaluation Metrics","order":25,"modules":["$45:props:state:queries:0:state:data:json:modules:125","$45:props:state:queries:0:state:data:json:modules:126","$45:props:state:queries:0:state:data:json:modules:127","$45:props:state:queries:0:state:data:json:modules:128","$45:props:state:queries:0:state:data:json:modules:129","$45:props:state:queries:0:state:data:json:modules:130","$45:props:state:queries:0:state:data:json:modules:131"]},{"title":"Chapter 19: Cross Validation And Resampling","order":26,"modules":["$45:props:state:queries:0:state:data:json:modules:132","$45:props:state:queries:0:state:data:json:modules:133","$45:props:state:queries:0:state:data:json:modules:134"]},{"title":"Chapter 19: Cross Validation Resampling","order":27,"modules":["$45:props:state:queries:0:state:data:json:modules:135","$45:props:state:queries:0:state:data:json:modules:136","$45:props:state:queries:0:state:data:json:modules:137","$45:props:state:queries:0:state:data:json:modules:138"]},{"title":"Chapter 20: Hyperparameter Optimization","order":28,"modules":["$45:props:state:queries:0:state:data:json:modules:139","$45:props:state:queries:0:state:data:json:modules:140","$45:props:state:queries:0:state:data:json:modules:141","$45:props:state:queries:0:state:data:json:modules:142","$45:props:state:queries:0:state:data:json:modules:143","$45:props:state:queries:0:state:data:json:modules:144"]},{"title":"Chapter 21: Clustering Algorithms","order":29,"modules":["$45:props:state:queries:0:state:data:json:modules:145","$45:props:state:queries:0:state:data:json:modules:146","$45:props:state:queries:0:state:data:json:modules:147","$45:props:state:queries:0:state:data:json:modules:148","$45:props:state:queries:0:state:data:json:modules:149","$45:props:state:queries:0:state:data:json:modules:150","$45:props:state:queries:0:state:data:json:modules:151","$45:props:state:queries:0:state:data:json:modules:152"]},{"title":"Chapter 22: Density Estimation And Mixture Models","order":30,"modules":["$45:props:state:queries:0:state:data:json:modules:153","$45:props:state:queries:0:state:data:json:modules:154","$45:props:state:queries:0:state:data:json:modules:155"]},{"title":"Chapter 22: Density Estimation Mixture Models","order":31,"modules":["$45:props:state:queries:0:state:data:json:modules:156","$45:props:state:queries:0:state:data:json:modules:157","$45:props:state:queries:0:state:data:json:modules:158"]},{"title":"Chapter 23: Dimensionality Reduction","order":32,"modules":["$45:props:state:queries:0:state:data:json:modules:159","$45:props:state:queries:0:state:data:json:modules:160","$45:props:state:queries:0:state:data:json:modules:161","$45:props:state:queries:0:state:data:json:modules:162","$45:props:state:queries:0:state:data:json:modules:163","$45:props:state:queries:0:state:data:json:modules:164","$45:props:state:queries:0:state:data:json:modules:165","$45:props:state:queries:0:state:data:json:modules:166"]},{"title":"Chapter 24: Manifold Learning","order":33,"modules":["$45:props:state:queries:0:state:data:json:modules:167","$45:props:state:queries:0:state:data:json:modules:168","$45:props:state:queries:0:state:data:json:modules:169","$45:props:state:queries:0:state:data:json:modules:170","$45:props:state:queries:0:state:data:json:modules:171","$45:props:state:queries:0:state:data:json:modules:172"]},{"title":"Chapter 25: Anomaly And Outlier Detection","order":34,"modules":["$45:props:state:queries:0:state:data:json:modules:173","$45:props:state:queries:0:state:data:json:modules:174","$45:props:state:queries:0:state:data:json:modules:175"]},{"title":"Chapter 25: Anomaly Outlier Detection","order":35,"modules":["$45:props:state:queries:0:state:data:json:modules:176","$45:props:state:queries:0:state:data:json:modules:177","$45:props:state:queries:0:state:data:json:modules:178"]},{"title":"Chapter 26: Bayesian Inference","order":36,"modules":["$45:props:state:queries:0:state:data:json:modules:179","$45:props:state:queries:0:state:data:json:modules:180","$45:props:state:queries:0:state:data:json:modules:181","$45:props:state:queries:0:state:data:json:modules:182","$45:props:state:queries:0:state:data:json:modules:183","$45:props:state:queries:0:state:data:json:modules:184","$45:props:state:queries:0:state:data:json:modules:185"]},{"title":"Chapter 27: Graphical Models","order":37,"modules":["$45:props:state:queries:0:state:data:json:modules:186","$45:props:state:queries:0:state:data:json:modules:187","$45:props:state:queries:0:state:data:json:modules:188","$45:props:state:queries:0:state:data:json:modules:189","$45:props:state:queries:0:state:data:json:modules:190","$45:props:state:queries:0:state:data:json:modules:191"]},{"title":"Chapter 28: Gaussian Processes","order":38,"modules":["$45:props:state:queries:0:state:data:json:modules:192","$45:props:state:queries:0:state:data:json:modules:193","$45:props:state:queries:0:state:data:json:modules:194","$45:props:state:queries:0:state:data:json:modules:195","$45:props:state:queries:0:state:data:json:modules:196","$45:props:state:queries:0:state:data:json:modules:197"]},{"title":"Chapter 29: Variational Inference","order":39,"modules":["$45:props:state:queries:0:state:data:json:modules:198","$45:props:state:queries:0:state:data:json:modules:199","$45:props:state:queries:0:state:data:json:modules:200","$45:props:state:queries:0:state:data:json:modules:201","$45:props:state:queries:0:state:data:json:modules:202","$45:props:state:queries:0:state:data:json:modules:203"]},{"title":"Chapter 30: Neural Network Foundations","order":40,"modules":["$45:props:state:queries:0:state:data:json:modules:204","$45:props:state:queries:0:state:data:json:modules:205","$45:props:state:queries:0:state:data:json:modules:206","$45:props:state:queries:0:state:data:json:modules:207","$45:props:state:queries:0:state:data:json:modules:208","$45:props:state:queries:0:state:data:json:modules:209","$45:props:state:queries:0:state:data:json:modules:210","$45:props:state:queries:0:state:data:json:modules:211"]},{"title":"Chapter 31: Backpropagation And Optimization","order":41,"modules":["$45:props:state:queries:0:state:data:json:modules:212","$45:props:state:queries:0:state:data:json:modules:213","$45:props:state:queries:0:state:data:json:modules:214","$45:props:state:queries:0:state:data:json:modules:215"]},{"title":"Chapter 31: Backpropagation Optimization","order":42,"modules":["$45:props:state:queries:0:state:data:json:modules:216","$45:props:state:queries:0:state:data:json:modules:217","$45:props:state:queries:0:state:data:json:modules:218","$45:props:state:queries:0:state:data:json:modules:219","$45:props:state:queries:0:state:data:json:modules:220"]},{"title":"Chapter 32: Regularization In Deep Learning","order":43,"modules":["$45:props:state:queries:0:state:data:json:modules:221","$45:props:state:queries:0:state:data:json:modules:222","$45:props:state:queries:0:state:data:json:modules:223","$45:props:state:queries:0:state:data:json:modules:224","$45:props:state:queries:0:state:data:json:modules:225","$45:props:state:queries:0:state:data:json:modules:226"]},{"title":"Chapter 33: Convolutional Neural Networks","order":44,"modules":["$45:props:state:queries:0:state:data:json:modules:227","$45:props:state:queries:0:state:data:json:modules:228","$45:props:state:queries:0:state:data:json:modules:229","$45:props:state:queries:0:state:data:json:modules:230","$45:props:state:queries:0:state:data:json:modules:231","$45:props:state:queries:0:state:data:json:modules:232","$45:props:state:queries:0:state:data:json:modules:233","$45:props:state:queries:0:state:data:json:modules:234"]},{"title":"Chapter 34: Recurrent Neural Networks","order":45,"modules":["$45:props:state:queries:0:state:data:json:modules:235","$45:props:state:queries:0:state:data:json:modules:236","$45:props:state:queries:0:state:data:json:modules:237","$45:props:state:queries:0:state:data:json:modules:238","$45:props:state:queries:0:state:data:json:modules:239","$45:props:state:queries:0:state:data:json:modules:240","$45:props:state:queries:0:state:data:json:modules:241","$45:props:state:queries:0:state:data:json:modules:242"]},{"title":"Chapter 35: Attention And Transformers","order":46,"modules":["$45:props:state:queries:0:state:data:json:modules:243","$45:props:state:queries:0:state:data:json:modules:244","$45:props:state:queries:0:state:data:json:modules:245","$45:props:state:queries:0:state:data:json:modules:246","$45:props:state:queries:0:state:data:json:modules:247","$45:props:state:queries:0:state:data:json:modules:248","$45:props:state:queries:0:state:data:json:modules:249"]},{"title":"Chapter 35: Attention Transformers","order":47,"modules":["$45:props:state:queries:0:state:data:json:modules:250"]},{"title":"Chapter 36: Generative Models","order":48,"modules":["$45:props:state:queries:0:state:data:json:modules:251","$45:props:state:queries:0:state:data:json:modules:252","$45:props:state:queries:0:state:data:json:modules:253","$45:props:state:queries:0:state:data:json:modules:254","$45:props:state:queries:0:state:data:json:modules:255"]},{"title":"Chapter 37: Semi Supervised And Self Supervised Learning","order":49,"modules":["$45:props:state:queries:0:state:data:json:modules:256"]},{"title":"Chapter 37: Semi Supervised Self Supervised Learning","order":50,"modules":["$45:props:state:queries:0:state:data:json:modules:257","$45:props:state:queries:0:state:data:json:modules:258","$45:props:state:queries:0:state:data:json:modules:259","$45:props:state:queries:0:state:data:json:modules:260","$45:props:state:queries:0:state:data:json:modules:261"]},{"title":"Chapter 38: Transfer Learning And Domain Adaptation","order":51,"modules":["$45:props:state:queries:0:state:data:json:modules:262"]},{"title":"Chapter 38: Transfer Learning Domain Adaptation","order":52,"modules":["$45:props:state:queries:0:state:data:json:modules:263","$45:props:state:queries:0:state:data:json:modules:264","$45:props:state:queries:0:state:data:json:modules:265","$45:props:state:queries:0:state:data:json:modules:266","$45:props:state:queries:0:state:data:json:modules:267"]},{"title":"Chapter 39: Reinforcement Learning","order":53,"modules":["$45:props:state:queries:0:state:data:json:modules:268","$45:props:state:queries:0:state:data:json:modules:269","$45:props:state:queries:0:state:data:json:modules:270","$45:props:state:queries:0:state:data:json:modules:271","$45:props:state:queries:0:state:data:json:modules:272","$45:props:state:queries:0:state:data:json:modules:273","$45:props:state:queries:0:state:data:json:modules:274","$45:props:state:queries:0:state:data:json:modules:275"]},{"title":"Chapter 40: Recommendation Systems","order":54,"modules":["$45:props:state:queries:0:state:data:json:modules:276","$45:props:state:queries:0:state:data:json:modules:277","$45:props:state:queries:0:state:data:json:modules:278","$45:props:state:queries:0:state:data:json:modules:279","$45:props:state:queries:0:state:data:json:modules:280","$45:props:state:queries:0:state:data:json:modules:281"]},{"title":"Chapter 41: Feature Engineering And Selection","order":55,"modules":["$45:props:state:queries:0:state:data:json:modules:282","$45:props:state:queries:0:state:data:json:modules:283","$45:props:state:queries:0:state:data:json:modules:284","$45:props:state:queries:0:state:data:json:modules:285","$45:props:state:queries:0:state:data:json:modules:286","$45:props:state:queries:0:state:data:json:modules:287"]},{"title":"Chapter 41: Feature Engineering Selection","order":56,"modules":["$45:props:state:queries:0:state:data:json:modules:288","$45:props:state:queries:0:state:data:json:modules:289","$45:props:state:queries:0:state:data:json:modules:290","$45:props:state:queries:0:state:data:json:modules:291","$45:props:state:queries:0:state:data:json:modules:292","$45:props:state:queries:0:state:data:json:modules:293","$45:props:state:queries:0:state:data:json:modules:294","$45:props:state:queries:0:state:data:json:modules:295"]},{"title":"Chapter 42: Automl Neural Architecture Search","order":57,"modules":["$45:props:state:queries:0:state:data:json:modules:296","$45:props:state:queries:0:state:data:json:modules:297","$45:props:state:queries:0:state:data:json:modules:298","$45:props:state:queries:0:state:data:json:modules:299","$45:props:state:queries:0:state:data:json:modules:300","$45:props:state:queries:0:state:data:json:modules:301"]},{"title":"Chapter 43: Ml Interpretability And Fairness","order":58,"modules":["$45:props:state:queries:0:state:data:json:modules:302"]},{"title":"Chapter 43: Ml Interpretability Fairness","order":59,"modules":["$45:props:state:queries:0:state:data:json:modules:303","$45:props:state:queries:0:state:data:json:modules:304","$45:props:state:queries:0:state:data:json:modules:305","$45:props:state:queries:0:state:data:json:modules:306","$45:props:state:queries:0:state:data:json:modules:307"]},{"title":"Chapter 44: Research Frontiers","order":60,"modules":["$45:props:state:queries:0:state:data:json:modules:308","$45:props:state:queries:0:state:data:json:modules:309","$45:props:state:queries:0:state:data:json:modules:310","$45:props:state:queries:0:state:data:json:modules:311","$45:props:state:queries:0:state:data:json:modules:312","$45:props:state:queries:0:state:data:json:modules:313"]},{"title":"Chapter 45: Ml Engineering Career","order":61,"modules":["$45:props:state:queries:0:state:data:json:modules:314","$45:props:state:queries:0:state:data:json:modules:315","$45:props:state:queries:0:state:data:json:modules:316","$45:props:state:queries:0:state:data:json:modules:317","$45:props:state:queries:0:state:data:json:modules:318","$45:props:state:queries:0:state:data:json:modules:319","$45:props:state:queries:0:state:data:json:modules:320","$45:props:state:queries:0:state:data:json:modules:321","$45:props:state:queries:0:state:data:json:modules:322"]},{"title":"Applied ML engineering: validate data releases","order":62,"modules":["$45:props:state:queries:0:state:data:json:modules:323","$45:props:state:queries:0:state:data:json:modules:324","$45:props:state:queries:0:state:data:json:modules:325","$45:props:state:queries:0:state:data:json:modules:326","$45:props:state:queries:0:state:data:json:modules:327"]}]},"meta":{"values":{"createdAt":["Date"],"updatedAt":["Date"],"modules.0.createdAt":["Date"],"modules.0.updatedAt":["Date"],"modules.1.createdAt":["Date"],"modules.1.updatedAt":["Date"],"modules.2.createdAt":["Date"],"modules.2.updatedAt":["Date"],"modules.3.createdAt":["Date"],"modules.3.updatedAt":["Date"],"modules.4.createdAt":["Date"],"modules.4.updatedAt":["Date"],"modules.5.createdAt":["Date"],"modules.5.updatedAt":["Date"],"modules.6.createdAt":["Date"],"modules.6.updatedAt":["Date"],"modules.7.createdAt":["Date"],"modules.7.updatedAt":["Date"],"modules.8.createdAt":["Date"],"modules.8.updatedAt":["Date"],"modules.9.createdAt":["Date"],"modules.9.updatedAt":["Date"],"modules.10.createdAt":["Date"],"modules.10.updatedAt":["Date"],"modules.11.createdAt":["Date"],"modules.11.updatedAt":["Date"],"modules.12.createdAt":["Date"],"modules.12.updatedAt":["Date"],"modules.13.createdAt":["Date"],"modules.13.updatedAt":["Date"],"modules.14.createdAt":["Date"],"modules.14.updatedAt":["Date"],"modules.15.createdAt":["Date"],"modules.15.updatedAt":["Date"],"modules.16.createdAt":["Date"],"modules.16.updatedAt":["Date"],"modules.17.createdAt":["Date"],"modules.17.updatedAt":["Date"],"modules.18.createdAt":["Date"],"modules.18.updatedAt":["Date"],"modules.19.createdAt":["Date"],"modules.19.updatedAt":["Date"],"modules.20.createdAt":["Date"],"modules.20.updatedAt":["Date"],"modules.21.createdAt":["Date"],"modules.21.updatedAt":["Date"],"modules.22.createdAt":["Date"],"modules.22.updatedAt":["Date"],"modules.23.createdAt":["Date"],"modules.23.updatedAt":["Date"],"modules.24.createdAt":["Date"],"modules.24.updatedAt":["Date"],"modules.25.createdAt":["Date"],"modules.25.updatedAt":["Date"],"modules.26.createdAt":["Date"],"modules.26.updatedAt":["Date"],"modules.27.createdAt":["Date"],"modules.27.updatedAt":["Date"],"modules.28.createdAt":["Date"],"modules.28.updatedAt":["Date"],"modules.29.createdAt":["Date"],"modules.29.updatedAt":["Date"],"modules.30.createdAt":["Date"],"modules.30.updatedAt":["Date"],"modules.31.createdAt":["Date"],"modules.31.updatedAt":["Date"],"modules.32.createdAt":["Date"],"modules.32.updatedAt":["Date"],"modules.33.createdAt":["Date"],"modules.33.updatedAt":["Date"],"modules.34.createdAt":["Date"],"modules.34.updatedAt":["Date"],"modules.35.createdAt":["Date"],"modules.35.updatedAt":["Date"],"modules.36.createdAt":["Date"],"modules.36.updatedAt":["Date"],"modules.37.createdAt":["Date"],"modules.37.updatedAt":["Date"],"modules.38.createdAt":["Date"],"modules.38.updatedAt":["Date"],"modules.39.createdAt":["Date"],"modules.39.updatedAt":["Date"],"modules.40.createdAt":["Date"],"modules.40.updatedAt":["Date"],"modules.41.createdAt":["Date"],"modules.41.updatedAt":["Date"],"modules.42.createdAt":["Date"],"modules.42.updatedAt":["Date"],"modules.43.createdAt":["Date"],"modules.43.updatedAt":["Date"],"modules.44.createdAt":["Date"],"modules.44.updatedAt":["Date"],"modules.45.createdAt":["Date"],"modules.45.updatedAt":["Date"],"modules.46.createdAt":["Date"],"modules.46.updatedAt":["Date"],"modules.47.createdAt":["Date"],"modules.47.updatedAt":["Date"],"modules.48.createdAt":["Date"],"modules.48.updatedAt":["Date"],"modules.49.createdAt":["Date"],"modules.49.updatedAt":["Date"],"modules.50.createdAt":["Date"],"modules.50.updatedAt":["Date"],"modules.51.createdAt":["Date"],"modules.51.updatedAt":["Date"],"modules.52.createdAt":["Date"],"modules.52.updatedAt":["Date"],"modules.53.createdAt":["Date"],"modules.53.updatedAt":["Date"],"modules.54.createdAt":["Date"],"modules.54.updatedAt":["Date"],"modules.55.createdAt":["Date"],"modules.55.updatedAt":["Date"],"modules.56.createdAt":["Date"],"modules.56.updatedAt":["Date"],"modules.57.createdAt":["Date"],"modules.57.updatedAt":["Date"],"modules.58.createdAt":["Date"],"modules.58.updatedAt":["Date"],"modules.59.createdAt":["Date"],"modules.59.updatedAt":["Date"],"modules.60.createdAt":["Date"],"modules.60.updatedAt":["Date"],"modules.61.createdAt":["Date"],"modules.61.updatedAt":["Date"],"modules.62.createdAt":["Date"],"modules.62.updatedAt":["Date"],"modules.63.createdAt":["Date"],"modules.63.updatedAt":["Date"],"modules.64.createdAt":["Date"],"modules.64.updatedAt":["Date"],"modules.65.createdAt":["Date"],"modules.65.updatedAt":["Date"],"modules.66.createdAt":["Date"],"modules.66.updatedAt":["Date"],"modules.67.createdAt":["Date"],"modules.67.updatedAt":["Date"],"modules.68.createdAt":["Date"],"modules.68.updatedAt":["Date"],"modules.69.createdAt":["Date"],"modules.69.updatedAt":["Date"],"modules.70.createdAt":["Date"],"modules.70.updatedAt":["Date"],"modules.71.createdAt":["Date"],"modules.71.updatedAt":["Date"],"modules.72.createdAt":["Date"],"modules.72.updatedAt":["Date"],"modules.73.createdAt":["Date"],"modules.73.updatedAt":["Date"],"modules.74.createdAt":["Date"],"modules.74.updatedAt":["Date"],"modules.75.createdAt":["Date"],"modules.75.updatedAt":["Date"],"modules.76.createdAt":["Date"],"modules.76.updatedAt":["Date"],"modules.77.createdAt":["Date"],"modules.77.updatedAt":["Date"],"modules.78.createdAt":["Date"],"modules.78.updatedAt":["Date"],"modules.79.createdAt":["Date"],"modules.79.updatedAt":["Date"],"modules.80.createdAt":["Date"],"modules.80.updatedAt":["Date"],"modules.81.createdAt":["Date"],"modules.81.updatedAt":["Date"],"modules.82.createdAt":["Date"],"modules.82.updatedAt":["Date"],"modules.83.createdAt":["Date"],"modules.83.updatedAt":["Date"],"modules.84.createdAt":["Date"],"modules.84.updatedAt":["Date"],"modules.85.createdAt":["Date"],"modules.85.updatedAt":["Date"],"modules.86.createdAt":["Date"],"modules.86.updatedAt":["Date"],"modules.87.createdAt":["Date"],"modules.87.updatedAt":["Date"],"modules.88.createdAt":["Date"],"modules.88.updatedAt":["Date"],"modules.89.createdAt":["Date"],"modules.89.updatedAt":["Date"],"modules.90.createdAt":["Date"],"modules.90.updatedAt":["Date"],"modules.91.createdAt":["Date"],"modules.91.updatedAt":["Date"],"modules.92.createdAt":["Date"],"modules.92.updatedAt":["Date"],"modules.93.createdAt":["Date"],"modules.93.updatedAt":["Date"],"modules.94.createdAt":["Date"],"modules.94.updatedAt":["Date"],"modules.95.createdAt":["Date"],"modules.95.updatedAt":["Date"],"modules.96.createdAt":["Date"],"modules.96.updatedAt":["Date"],"modules.97.createdAt":["Date"],"modules.97.updatedAt":["Date"],"modules.98.createdAt":["Date"],"modules.98.updatedAt":["Date"],"modules.99.createdAt":["Date"],"modules.99.updatedAt":["Date"],"modules.100.createdAt":["Date"],"modules.100.updatedAt":["Date"],"modules.101.createdAt":["Date"],"modules.101.updatedAt":["Date"],"modules.102.createdAt":["Date"],"modules.102.updatedAt":["Date"],"modules.103.createdAt":["Date"],"modules.103.updatedAt":["Date"],"modules.104.createdAt":["Date"],"modules.104.updatedAt":["Date"],"modules.105.createdAt":["Date"],"modules.105.updatedAt":["Date"],"modules.106.createdAt":["Date"],"modules.106.updatedAt":["Date"],"modules.107.createdAt":["Date"],"modules.107.updatedAt":["Date"],"modules.108.createdAt":["Date"],"modules.108.updatedAt":["Date"],"modules.109.createdAt":["Date"],"modules.109.updatedAt":["Date"],"modules.110.createdAt":["Date"],"modules.110.updatedAt":["Date"],"modules.111.createdAt":["Date"],"modules.111.updatedAt":["Date"],"modules.112.createdAt":["Date"],"modules.112.updatedAt":["Date"],"modules.113.createdAt":["Date"],"modules.113.updatedAt":["Date"],"modules.114.createdAt":["Date"],"modules.114.updatedAt":["Date"],"modules.115.createdAt":["Date"],"modules.115.updatedAt":["Date"],"modules.116.createdAt":["Date"],"modules.116.updatedAt":["Date"],"modules.117.createdAt":["Date"],"modules.117.updatedAt":["Date"],"modules.118.createdAt":["Date"],"modules.118.updatedAt":["Date"],"modules.119.createdAt":["Date"],"modules.119.updatedAt":["Date"],"modules.120.createdAt":["Date"],"modules.120.updatedAt":["Date"],"modules.121.createdAt":["Date"],"modules.121.updatedAt":["Date"],"modules.122.createdAt":["Date"],"modules.122.updatedAt":["Date"],"modules.123.createdAt":["Date"],"modules.123.updatedAt":["Date"],"modules.124.createdAt":["Date"],"modules.124.updatedAt":["Date"],"modules.125.createdAt":["Date"],"modules.125.updatedAt":["Date"],"modules.126.createdAt":["Date"],"modules.126.updatedAt":["Date"],"modules.127.createdAt":["Date"],"modules.127.updatedAt":["Date"],"modules.128.createdAt":["Date"],"modules.128.updatedAt":["Date"],"modules.129.createdAt":["Date"],"modules.129.updatedAt":["Date"],"modules.130.createdAt":["Date"],"modules.130.updatedAt":["Date"],"modules.131.createdAt":["Date"],"modules.131.updatedAt":["Date"],"modules.132.createdAt":["Date"],"modules.132.updatedAt":["Date"],"modules.133.createdAt":["Date"],"modules.133.updatedAt":["Date"],"modules.134.createdAt":["Date"],"modules.134.updatedAt":["Date"],"modules.135.createdAt":["Date"],"modules.135.updatedAt":["Date"],"modules.136.createdAt":["Date"],"modules.136.updatedAt":["Date"],"modules.137.createdAt":["Date"],"modules.137.updatedAt":["Date"],"modules.138.createdAt":["Date"],"modules.138.updatedAt":["Date"],"modules.139.createdAt":["Date"],"modules.139.updatedAt":["Date"],"modules.140.createdAt":["Date"],"modules.140.updatedAt":["Date"],"modules.141.createdAt":["Date"],"modules.141.updatedAt":["Date"],"modules.142.createdAt":["Date"],"modules.142.updatedAt":["Date"],"modules.143.createdAt":["Date"],"modules.143.updatedAt":["Date"],"modules.144.createdAt":["Date"],"modules.144.updatedAt":["Date"],"modules.145.createdAt":["Date"],"modules.145.updatedAt":["Date"],"modules.146.createdAt":["Date"],"modules.146.updatedAt":["Date"],"modules.147.createdAt":["Date"],"modules.147.updatedAt":["Date"],"modules.148.createdAt":["Date"],"modules.148.updatedAt":["Date"],"modules.149.createdAt":["Date"],"modules.149.updatedAt":["Date"],"modules.150.createdAt":["Date"],"modules.150.updatedAt":["Date"],"modules.151.createdAt":["Date"],"modules.151.updatedAt":["Date"],"modules.152.createdAt":["Date"],"modules.152.updatedAt":["Date"],"modules.153.createdAt":["Date"],"modules.153.updatedAt":["Date"],"modules.154.createdAt":["Date"],"modules.154.updatedAt":["Date"],"modules.155.createdAt":["Date"],"modules.155.updatedAt":["Date"],"modules.156.createdAt":["Date"],"modules.156.updatedAt":["Date"],"modules.157.createdAt":["Date"],"modules.157.updatedAt":["Date"],"modules.158.createdAt":["Date"],"modules.158.updatedAt":["Date"],"modules.159.createdAt":["Date"],"modules.159.updatedAt":["Date"],"modules.160.createdAt":["Date"],"modules.160.updatedAt":["Date"],"modules.161.createdAt":["Date"],"modules.161.updatedAt":["Date"],"modules.162.createdAt":["Date"],"modules.162.updatedAt":["Date"],"modules.163.createdAt":["Date"],"modules.163.updatedAt":["Date"],"modules.164.createdAt":["Date"],"modules.164.updatedAt":["Date"],"modules.165.createdAt":["Date"],"modules.165.updatedAt":["Date"],"modules.166.createdAt":["Date"],"modules.166.updatedAt":["Date"],"modules.167.createdAt":["Date"],"modules.167.updatedAt":["Date"],"modules.168.createdAt":["Date"],"modules.168.updatedAt":["Date"],"modules.169.createdAt":["Date"],"modules.169.updatedAt":["Date"],"modules.170.createdAt":["Date"],"modules.170.updatedAt":["Date"],"modules.171.createdAt":["Date"],"modules.171.updatedAt":["Date"],"modules.172.createdAt":["Date"],"modules.172.updatedAt":["Date"],"modules.173.createdAt":["Date"],"modules.173.updatedAt":["Date"],"modules.174.createdAt":["Date"],"modules.174.updatedAt":["Date"],"modules.175.createdAt":["Date"],"modules.175.updatedAt":["Date"],"modules.176.createdAt":["Date"],"modules.176.updatedAt":["Date"],"modules.177.createdAt":["Date"],"modules.177.updatedAt":["Date"],"modules.178.createdAt":["Date"],"modules.178.updatedAt":["Date"],"modules.179.createdAt":["Date"],"modules.179.updatedAt":["Date"],"modules.180.createdAt":["Date"],"modules.180.updatedAt":["Date"],"modules.181.createdAt":["Date"],"modules.181.updatedAt":["Date"],"modules.182.createdAt":["Date"],"modules.182.updatedAt":["Date"],"modules.183.createdAt":["Date"],"modules.183.updatedAt":["Date"],"modules.184.createdAt":["Date"],"modules.184.updatedAt":["Date"],"modules.185.createdAt":["Date"],"modules.185.updatedAt":["Date"],"modules.186.createdAt":["Date"],"modules.186.updatedAt":["Date"],"modules.187.createdAt":["Date"],"modules.187.updatedAt":["Date"],"modules.188.createdAt":["Date"],"modules.188.updatedAt":["Date"],"modules.189.createdAt":["Date"],"modules.189.updatedAt":["Date"],"modules.190.createdAt":["Date"],"modules.190.updatedAt":["Date"],"modules.191.createdAt":["Date"],"modules.191.updatedAt":["Date"],"modules.192.createdAt":["Date"],"modules.192.updatedAt":["Date"],"modules.193.createdAt":["Date"],"modules.193.updatedAt":["Date"],"modules.194.createdAt":["Date"],"modules.194.updatedAt":["Date"],"modules.195.createdAt":["Date"],"modules.195.updatedAt":["Date"],"modules.196.createdAt":["Date"],"modules.196.updatedAt":["Date"],"modules.197.createdAt":["Date"],"modules.197.updatedAt":["Date"],"modules.198.createdAt":["Date"],"modules.198.updatedAt":["Date"],"modules.199.createdAt":["Date"],"modules.199.updatedAt":["Date"],"modules.200.createdAt":["Date"],"modules.200.updatedAt":["Date"],"modules.201.createdAt":["Date"],"modules.201.updatedAt":["Date"],"modules.202.createdAt":["Date"],"modules.202.updatedAt":["Date"],"modules.203.createdAt":["Date"],"modules.203.updatedAt":["Date"],"modules.204.createdAt":["Date"],"modules.204.updatedAt":["Date"],"modules.205.createdAt":["Date"],"modules.205.updatedAt":["Date"],"modules.206.createdAt":["Date"],"modules.206.updatedAt":["Date"],"modules.207.createdAt":["Date"],"modules.207.updatedAt":["Date"],"modules.208.createdAt":["Date"],"modules.208.updatedAt":["Date"],"modules.209.createdAt":["Date"],"modules.209.updatedAt":["Date"],"modules.210.createdAt":["Date"],"modules.210.updatedAt":["Date"],"modules.211.createdAt":["Date"],"modules.211.updatedAt":["Date"],"modules.212.createdAt":["Date"],"modules.212.updatedAt":["Date"],"modules.213.createdAt":["Date"],"modules.213.updatedAt":["Date"],"modules.214.createdAt":["Date"],"modules.214.updatedAt":["Date"],"modules.215.createdAt":["Date"],"modules.215.updatedAt":["Date"],"modules.216.createdAt":["Date"],"modules.216.updatedAt":["Date"],"modules.217.createdAt":["Date"],"modules.217.updatedAt":["Date"],"modules.218.createdAt":["Date"],"modules.218.updatedAt":["Date"],"modules.219.createdAt":["Date"],"modules.219.updatedAt":["Date"],"modules.220.createdAt":["Date"],"modules.220.updatedAt":["Date"],"modules.221.createdAt":["Date"],"modules.221.updatedAt":["Date"],"modules.222.createdAt":["Date"],"modules.222.updatedAt":["Date"],"modules.223.createdAt":["Date"],"modules.223.updatedAt":["Date"],"modules.224.createdAt":["Date"],"modules.224.updatedAt":["Date"],"modules.225.createdAt":["Date"],"modules.225.updatedAt":["Date"],"modules.226.createdAt":["Date"],"modules.226.updatedAt":["Date"],"modules.227.createdAt":["Date"],"modules.227.updatedAt":["Date"],"modules.228.createdAt":["Date"],"modules.228.updatedAt":["Date"],"modules.229.createdAt":["Date"],"modules.229.updatedAt":["Date"],"modules.230.createdAt":["Date"],"modules.230.updatedAt":["Date"],"modules.231.createdAt":["Date"],"modules.231.updatedAt":["Date"],"modules.232.createdAt":["Date"],"modules.232.updatedAt":["Date"],"modules.233.createdAt":["Date"],"modules.233.updatedAt":["Date"],"modules.234.createdAt":["Date"],"modules.234.updatedAt":["Date"],"modules.235.createdAt":["Date"],"modules.235.updatedAt":["Date"],"modules.236.createdAt":["Date"],"modules.236.updatedAt":["Date"],"modules.237.createdAt":["Date"],"modules.237.updatedAt":["Date"],"modules.238.createdAt":["Date"],"modules.238.updatedAt":["Date"],"modules.239.createdAt":["Date"],"modules.239.updatedAt":["Date"],"modules.240.createdAt":["Date"],"modules.240.updatedAt":["Date"],"modules.241.createdAt":["Date"],"modules.241.updatedAt":["Date"],"modules.242.createdAt":["Date"],"modules.242.updatedAt":["Date"],"modules.243.createdAt":["Date"],"modules.243.updatedAt":["Date"],"modules.244.createdAt":["Date"],"modules.244.updatedAt":["Date"],"modules.245.createdAt":["Date"],"modules.245.updatedAt":["Date"],"modules.246.createdAt":["Date"],"modules.246.updatedAt":["Date"],"modules.247.createdAt":["Date"],"modules.247.updatedAt":["Date"],"modules.248.createdAt":["Date"],"modules.248.updatedAt":["Date"],"modules.249.createdAt":["Date"],"modules.249.updatedAt":["Date"],"modules.250.createdAt":["Date"],"modules.250.updatedAt":["Date"],"modules.251.createdAt":["Date"],"modules.251.updatedAt":["Date"],"modules.252.createdAt":["Date"],"modules.252.updatedAt":["Date"],"modules.253.createdAt":["Date"],"modules.253.updatedAt":["Date"],"modules.254.createdAt":["Date"],"modules.254.updatedAt":["Date"],"modules.255.createdAt":["Date"],"modules.255.updatedAt":["Date"],"modules.256.createdAt":["Date"],"modules.256.updatedAt":["Date"],"modules.257.createdAt":["Date"],"modules.257.updatedAt":["Date"],"modules.258.createdAt":["Date"],"modules.258.updatedAt":["Date"],"modules.259.createdAt":["Date"],"modules.259.updatedAt":["Date"],"modules.260.createdAt":["Date"],"modules.260.updatedAt":["Date"],"modules.261.createdAt":["Date"],"modules.261.updatedAt":["Date"],"modules.262.createdAt":["Date"],"modules.262.updatedAt":["Date"],"modules.263.createdAt":["Date"],"modules.263.updatedAt":["Date"],"modules.264.createdAt":["Date"],"modules.264.updatedAt":["Date"],"modules.265.createdAt":["Date"],"modules.265.updatedAt":["Date"],"modules.266.createdAt":["Date"],"modules.266.updatedAt":["Date"],"modules.267.createdAt":["Date"],"modules.267.updatedAt":["Date"],"modules.268.createdAt":["Date"],"modules.268.updatedAt":["Date"],"modules.269.createdAt":["Date"],"modules.269.updatedAt":["Date"],"modules.270.createdAt":["Date"],"modules.270.updatedAt":["Date"],"modules.271.createdAt":["Date"],"modules.271.updatedAt":["Date"],"modules.272.createdAt":["Date"],"modules.272.updatedAt":["Date"],"modules.273.createdAt":["Date"],"modules.273.updatedAt":["Date"],"modules.274.createdAt":["Date"],"modules.274.updatedAt":["Date"],"modules.275.createdAt":["Date"],"modules.275.updatedAt":["Date"],"modules.276.createdAt":["Date"],"modules.276.updatedAt":["Date"],"modules.277.createdAt":["Date"],"modules.277.updatedAt":["Date"],"modules.278.createdAt":["Date"],"modules.278.updatedAt":["Date"],"modules.279.createdAt":["Date"],"modules.279.updatedAt":["Date"],"modules.280.createdAt":["Date"],"modules.280.updatedAt":["Date"],"modules.281.createdAt":["Date"],"modules.281.updatedAt":["Date"],"modules.282.createdAt":["Date"],"modules.282.updatedAt":["Date"],"modules.283.createdAt":["Date"],"modules.283.updatedAt":["Date"],"modules.284.createdAt":["Date"],"modules.284.updatedAt":["Date"],"modules.285.createdAt":["Date"],"modules.285.updatedAt":["Date"],"modules.286.createdAt":["Date"],"modules.286.updatedAt":["Date"],"modules.287.createdAt":["Date"],"modules.287.updatedAt":["Date"],"modules.288.createdAt":["Date"],"modules.288.updatedAt":["Date"],"modules.289.createdAt":["Date"],"modules.289.updatedAt":["Date"],"modules.290.createdAt":["Date"],"modules.290.updatedAt":["Date"],"modules.291.createdAt":["Date"],"modules.291.updatedAt":["Date"],"modules.292.createdAt":["Date"],"modules.292.updatedAt":["Date"],"modules.293.createdAt":["Date"],"modules.293.updatedAt":["Date"],"modules.294.createdAt":["Date"],"modules.294.updatedAt":["Date"],"modules.295.createdAt":["Date"],"modules.295.updatedAt":["Date"],"modules.296.createdAt":["Date"],"modules.296.updatedAt":["Date"],"modules.297.createdAt":["Date"],"modules.297.updatedAt":["Date"],"modules.298.createdAt":["Date"],"modules.298.updatedAt":["Date"],"modules.299.createdAt":["Date"],"modules.299.updatedAt":["Date"],"modules.300.createdAt":["Date"],"modules.300.updatedAt":["Date"],"modules.301.createdAt":["Date"],"modules.301.updatedAt":["Date"],"modules.302.createdAt":["Date"],"modules.302.updatedAt":["Date"],"modules.303.createdAt":["Date"],"modules.303.updatedAt":["Date"],"modules.304.createdAt":["Date"],"modules.304.updatedAt":["Date"],"modules.305.createdAt":["Date"],"modules.305.updatedAt":["Date"],"modules.306.createdAt":["Date"],"modules.306.updatedAt":["Date"],"modules.307.createdAt":["Date"],"modules.307.updatedAt":["Date"],"modules.308.createdAt":["Date"],"modules.308.updatedAt":["Date"],"modules.309.createdAt":["Date"],"modules.309.updatedAt":["Date"],"modules.310.createdAt":["Date"],"modules.310.updatedAt":["Date"],"modules.311.createdAt":["Date"],"modules.311.updatedAt":["Date"],"modules.312.createdAt":["Date"],"modules.312.updatedAt":["Date"],"modules.313.createdAt":["Date"],"modules.313.updatedAt":["Date"],"modules.314.createdAt":["Date"],"modules.314.updatedAt":["Date"],"modules.315.createdAt":["Date"],"modules.315.updatedAt":["Date"],"modules.316.createdAt":["Date"],"modules.316.updatedAt":["Date"],"modules.317.createdAt":["Date"],"modules.317.updatedAt":["Date"],"modules.318.createdAt":["Date"],"modules.318.updatedAt":["Date"],"modules.319.createdAt":["Date"],"modules.319.updatedAt":["Date"],"modules.320.createdAt":["Date"],"modules.320.updatedAt":["Date"],"modules.321.createdAt":["Date"],"modules.321.updatedAt":["Date"],"modules.322.createdAt":["Date"],"modules.322.updatedAt":["Date"],"modules.323.createdAt":["Date"],"modules.323.updatedAt":["Date"],"modules.324.createdAt":["Date"],"modules.324.updatedAt":["Date"],"modules.325.createdAt":["Date"],"modules.325.updatedAt":["Date"],"modules.326.createdAt":["Date"],"modules.326.updatedAt":["Date"],"modules.327.createdAt":["Date"],"modules.327.updatedAt":["Date"],"sections.0.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.0.createdAt","sections.0.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.0.updatedAt","sections.0.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.1.createdAt","sections.0.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.1.updatedAt","sections.0.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.2.createdAt","sections.0.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.2.updatedAt","sections.0.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.3.createdAt","sections.0.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.3.updatedAt","sections.1.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.4.createdAt","sections.1.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.4.updatedAt","sections.1.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.5.createdAt","sections.1.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.5.updatedAt","sections.1.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.6.createdAt","sections.1.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.6.updatedAt","sections.2.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.7.createdAt","sections.2.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.7.updatedAt","sections.2.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.8.createdAt","sections.2.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.8.updatedAt","sections.2.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.9.createdAt","sections.2.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.9.updatedAt","sections.2.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.10.createdAt","sections.2.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.10.updatedAt","sections.2.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.11.createdAt","sections.2.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.11.updatedAt","sections.2.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.12.createdAt","sections.2.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.12.updatedAt","sections.2.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.13.createdAt","sections.2.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.13.updatedAt","sections.2.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.14.createdAt","sections.2.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.14.updatedAt","sections.3.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.15.createdAt","sections.3.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.15.updatedAt","sections.3.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.16.createdAt","sections.3.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.16.updatedAt","sections.3.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.17.createdAt","sections.3.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.17.updatedAt","sections.3.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.18.createdAt","sections.3.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.18.updatedAt","sections.3.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.19.createdAt","sections.3.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.19.updatedAt","sections.4.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.20.createdAt","sections.4.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.20.updatedAt","sections.4.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.21.createdAt","sections.4.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.21.updatedAt","sections.4.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.22.createdAt","sections.4.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.22.updatedAt","sections.4.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.23.createdAt","sections.4.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.23.updatedAt","sections.4.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.24.createdAt","sections.4.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.24.updatedAt","sections.4.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.25.createdAt","sections.4.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.25.updatedAt","sections.4.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.26.createdAt","sections.4.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.26.updatedAt","sections.4.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.27.createdAt","sections.4.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.27.updatedAt","sections.4.modules.8.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.28.createdAt","sections.4.modules.8.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.28.updatedAt","sections.5.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.29.createdAt","sections.5.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.29.updatedAt","sections.5.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.30.createdAt","sections.5.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.30.updatedAt","sections.5.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.31.createdAt","sections.5.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.31.updatedAt","sections.5.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.32.createdAt","sections.5.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.32.updatedAt","sections.5.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.33.createdAt","sections.5.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.33.updatedAt","sections.5.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.34.createdAt","sections.5.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.34.updatedAt","sections.5.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.35.createdAt","sections.5.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.35.updatedAt","sections.5.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.36.createdAt","sections.5.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.36.updatedAt","sections.6.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.37.createdAt","sections.6.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.37.updatedAt","sections.6.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.38.createdAt","sections.6.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.38.updatedAt","sections.6.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.39.createdAt","sections.6.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.39.updatedAt","sections.6.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.40.createdAt","sections.6.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.40.updatedAt","sections.6.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.41.createdAt","sections.6.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.41.updatedAt","sections.6.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.42.createdAt","sections.6.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.42.updatedAt","sections.7.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.43.createdAt","sections.7.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.43.updatedAt","sections.7.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.44.createdAt","sections.7.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.44.updatedAt","sections.8.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.45.createdAt","sections.8.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.45.updatedAt","sections.8.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.46.createdAt","sections.8.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.46.updatedAt","sections.8.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.47.createdAt","sections.8.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.47.updatedAt","sections.8.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.48.createdAt","sections.8.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.48.updatedAt","sections.8.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.49.createdAt","sections.8.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.49.updatedAt","sections.8.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.50.createdAt","sections.8.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.50.updatedAt","sections.8.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.51.createdAt","sections.8.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.51.updatedAt","sections.9.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.52.createdAt","sections.9.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.52.updatedAt","sections.9.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.53.createdAt","sections.9.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.53.updatedAt","sections.9.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.54.createdAt","sections.9.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.54.updatedAt","sections.9.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.55.createdAt","sections.9.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.55.updatedAt","sections.9.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.56.createdAt","sections.9.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.56.updatedAt","sections.9.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.57.createdAt","sections.9.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.57.updatedAt","sections.10.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.58.createdAt","sections.10.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.58.updatedAt","sections.10.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.59.createdAt","sections.10.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.59.updatedAt","sections.11.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.60.createdAt","sections.11.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.60.updatedAt","sections.11.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.61.createdAt","sections.11.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.61.updatedAt","sections.11.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.62.createdAt","sections.11.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.62.updatedAt","sections.11.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.63.createdAt","sections.11.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.63.updatedAt","sections.11.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.64.createdAt","sections.11.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.64.updatedAt","sections.12.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.65.createdAt","sections.12.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.65.updatedAt","sections.12.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.66.createdAt","sections.12.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.66.updatedAt","sections.12.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.67.createdAt","sections.12.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.67.updatedAt","sections.12.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.68.createdAt","sections.12.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.68.updatedAt","sections.12.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.69.createdAt","sections.12.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.69.updatedAt","sections.12.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.70.createdAt","sections.12.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.70.updatedAt","sections.13.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.71.createdAt","sections.13.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.71.updatedAt","sections.14.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.72.createdAt","sections.14.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.72.updatedAt","sections.14.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.73.createdAt","sections.14.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.73.updatedAt","sections.14.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.74.createdAt","sections.14.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.74.updatedAt","sections.14.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.75.createdAt","sections.14.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.75.updatedAt","sections.14.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.76.createdAt","sections.14.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.76.updatedAt","sections.14.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.77.createdAt","sections.14.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.77.updatedAt","sections.14.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.78.createdAt","sections.14.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.78.updatedAt","sections.14.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.79.createdAt","sections.14.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.79.updatedAt","sections.15.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.80.createdAt","sections.15.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.80.updatedAt","sections.15.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.81.createdAt","sections.15.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.81.updatedAt","sections.15.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.82.createdAt","sections.15.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.82.updatedAt","sections.15.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.83.createdAt","sections.15.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.83.updatedAt","sections.15.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.84.createdAt","sections.15.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.84.updatedAt","sections.15.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.85.createdAt","sections.15.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.85.updatedAt","sections.15.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.86.createdAt","sections.15.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.86.updatedAt","sections.16.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.87.createdAt","sections.16.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.87.updatedAt","sections.16.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.88.createdAt","sections.16.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.88.updatedAt","sections.16.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.89.createdAt","sections.16.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.89.updatedAt","sections.16.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.90.createdAt","sections.16.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.90.updatedAt","sections.16.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.91.createdAt","sections.16.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.91.updatedAt","sections.16.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.92.createdAt","sections.16.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.92.updatedAt","sections.17.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.93.createdAt","sections.17.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.93.updatedAt","sections.17.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.94.createdAt","sections.17.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.94.updatedAt","sections.18.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.95.createdAt","sections.18.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.95.updatedAt","sections.18.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.96.createdAt","sections.18.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.96.updatedAt","sections.18.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.97.createdAt","sections.18.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.97.updatedAt","sections.18.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.98.createdAt","sections.18.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.98.updatedAt","sections.19.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.99.createdAt","sections.19.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.99.updatedAt","sections.19.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.100.createdAt","sections.19.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.100.updatedAt","sections.19.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.101.createdAt","sections.19.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.101.updatedAt","sections.19.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.102.createdAt","sections.19.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.102.updatedAt","sections.19.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.103.createdAt","sections.19.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.103.updatedAt","sections.19.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.104.createdAt","sections.19.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.104.updatedAt","sections.19.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.105.createdAt","sections.19.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.105.updatedAt","sections.19.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.106.createdAt","sections.19.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.106.updatedAt","sections.20.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.107.createdAt","sections.20.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.107.updatedAt","sections.20.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.108.createdAt","sections.20.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.108.updatedAt","sections.20.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.109.createdAt","sections.20.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.109.updatedAt","sections.21.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.110.createdAt","sections.21.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.110.updatedAt","sections.21.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.111.createdAt","sections.21.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.111.updatedAt","sections.21.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.112.createdAt","sections.21.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.112.updatedAt","sections.22.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.113.createdAt","sections.22.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.113.updatedAt","sections.22.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.114.createdAt","sections.22.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.114.updatedAt","sections.22.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.115.createdAt","sections.22.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.115.updatedAt","sections.22.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.116.createdAt","sections.22.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.116.updatedAt","sections.22.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.117.createdAt","sections.22.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.117.updatedAt","sections.22.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.118.createdAt","sections.22.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.118.updatedAt","sections.23.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.119.createdAt","sections.23.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.119.updatedAt","sections.23.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.120.createdAt","sections.23.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.120.updatedAt","sections.23.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.121.createdAt","sections.23.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.121.updatedAt","sections.23.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.122.createdAt","sections.23.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.122.updatedAt","sections.23.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.123.createdAt","sections.23.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.123.updatedAt","sections.23.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.124.createdAt","sections.23.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.124.updatedAt","sections.24.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.125.createdAt","sections.24.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.125.updatedAt","sections.24.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.126.createdAt","sections.24.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.126.updatedAt","sections.24.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.127.createdAt","sections.24.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.127.updatedAt","sections.24.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.128.createdAt","sections.24.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.128.updatedAt","sections.24.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.129.createdAt","sections.24.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.129.updatedAt","sections.24.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.130.createdAt","sections.24.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.130.updatedAt","sections.24.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.131.createdAt","sections.24.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.131.updatedAt","sections.25.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.132.createdAt","sections.25.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.132.updatedAt","sections.25.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.133.createdAt","sections.25.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.133.updatedAt","sections.25.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.134.createdAt","sections.25.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.134.updatedAt","sections.26.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.135.createdAt","sections.26.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.135.updatedAt","sections.26.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.136.createdAt","sections.26.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.136.updatedAt","sections.26.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.137.createdAt","sections.26.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.137.updatedAt","sections.26.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.138.createdAt","sections.26.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.138.updatedAt","sections.27.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.139.createdAt","sections.27.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.139.updatedAt","sections.27.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.140.createdAt","sections.27.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.140.updatedAt","sections.27.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.141.createdAt","sections.27.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.141.updatedAt","sections.27.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.142.createdAt","sections.27.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.142.updatedAt","sections.27.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.143.createdAt","sections.27.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.143.updatedAt","sections.27.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.144.createdAt","sections.27.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.144.updatedAt","sections.28.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.145.createdAt","sections.28.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.145.updatedAt","sections.28.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.146.createdAt","sections.28.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.146.updatedAt","sections.28.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.147.createdAt","sections.28.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.147.updatedAt","sections.28.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.148.createdAt","sections.28.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.148.updatedAt","sections.28.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.149.createdAt","sections.28.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.149.updatedAt","sections.28.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.150.createdAt","sections.28.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.150.updatedAt","sections.28.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.151.createdAt","sections.28.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.151.updatedAt","sections.28.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.152.createdAt","sections.28.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.152.updatedAt","sections.29.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.153.createdAt","sections.29.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.153.updatedAt","sections.29.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.154.createdAt","sections.29.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.154.updatedAt","sections.29.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.155.createdAt","sections.29.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.155.updatedAt","sections.30.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.156.createdAt","sections.30.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.156.updatedAt","sections.30.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.157.createdAt","sections.30.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.157.updatedAt","sections.30.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.158.createdAt","sections.30.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.158.updatedAt","sections.31.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.159.createdAt","sections.31.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.159.updatedAt","sections.31.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.160.createdAt","sections.31.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.160.updatedAt","sections.31.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.161.createdAt","sections.31.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.161.updatedAt","sections.31.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.162.createdAt","sections.31.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.162.updatedAt","sections.31.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.163.createdAt","sections.31.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.163.updatedAt","sections.31.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.164.createdAt","sections.31.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.164.updatedAt","sections.31.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.165.createdAt","sections.31.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.165.updatedAt","sections.31.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.166.createdAt","sections.31.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.166.updatedAt","sections.32.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.167.createdAt","sections.32.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.167.updatedAt","sections.32.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.168.createdAt","sections.32.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.168.updatedAt","sections.32.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.169.createdAt","sections.32.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.169.updatedAt","sections.32.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.170.createdAt","sections.32.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.170.updatedAt","sections.32.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.171.createdAt","sections.32.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.171.updatedAt","sections.32.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.172.createdAt","sections.32.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.172.updatedAt","sections.33.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.173.createdAt","sections.33.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.173.updatedAt","sections.33.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.174.createdAt","sections.33.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.174.updatedAt","sections.33.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.175.createdAt","sections.33.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.175.updatedAt","sections.34.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.176.createdAt","sections.34.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.176.updatedAt","sections.34.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.177.createdAt","sections.34.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.177.updatedAt","sections.34.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.178.createdAt","sections.34.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.178.updatedAt","sections.35.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.179.createdAt","sections.35.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.179.updatedAt","sections.35.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.180.createdAt","sections.35.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.180.updatedAt","sections.35.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.181.createdAt","sections.35.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.181.updatedAt","sections.35.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.182.createdAt","sections.35.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.182.updatedAt","sections.35.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.183.createdAt","sections.35.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.183.updatedAt","sections.35.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.184.createdAt","sections.35.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.184.updatedAt","sections.35.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.185.createdAt","sections.35.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.185.updatedAt","sections.36.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.186.createdAt","sections.36.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.186.updatedAt","sections.36.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.187.createdAt","sections.36.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.187.updatedAt","sections.36.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.188.createdAt","sections.36.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.188.updatedAt","sections.36.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.189.createdAt","sections.36.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.189.updatedAt","sections.36.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.190.createdAt","sections.36.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.190.updatedAt","sections.36.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.191.createdAt","sections.36.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.191.updatedAt","sections.37.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.192.createdAt","sections.37.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.192.updatedAt","sections.37.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.193.createdAt","sections.37.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.193.updatedAt","sections.37.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.194.createdAt","sections.37.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.194.updatedAt","sections.37.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.195.createdAt","sections.37.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.195.updatedAt","sections.37.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.196.createdAt","sections.37.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.196.updatedAt","sections.37.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.197.createdAt","sections.37.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.197.updatedAt","sections.38.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.198.createdAt","sections.38.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.198.updatedAt","sections.38.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.199.createdAt","sections.38.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.199.updatedAt","sections.38.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.200.createdAt","sections.38.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.200.updatedAt","sections.38.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.201.createdAt","sections.38.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.201.updatedAt","sections.38.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.202.createdAt","sections.38.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.202.updatedAt","sections.38.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.203.createdAt","sections.38.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.203.updatedAt","sections.39.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.204.createdAt","sections.39.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.204.updatedAt","sections.39.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.205.createdAt","sections.39.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.205.updatedAt","sections.39.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.206.createdAt","sections.39.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.206.updatedAt","sections.39.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.207.createdAt","sections.39.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.207.updatedAt","sections.39.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.208.createdAt","sections.39.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.208.updatedAt","sections.39.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.209.createdAt","sections.39.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.209.updatedAt","sections.39.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.210.createdAt","sections.39.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.210.updatedAt","sections.39.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.211.createdAt","sections.39.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.211.updatedAt","sections.40.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.212.createdAt","sections.40.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.212.updatedAt","sections.40.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.213.createdAt","sections.40.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.213.updatedAt","sections.40.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.214.createdAt","sections.40.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.214.updatedAt","sections.40.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.215.createdAt","sections.40.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.215.updatedAt","sections.41.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.216.createdAt","sections.41.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.216.updatedAt","sections.41.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.217.createdAt","sections.41.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.217.updatedAt","sections.41.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.218.createdAt","sections.41.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.218.updatedAt","sections.41.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.219.createdAt","sections.41.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.219.updatedAt","sections.41.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.220.createdAt","sections.41.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.220.updatedAt","sections.42.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.221.createdAt","sections.42.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.221.updatedAt","sections.42.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.222.createdAt","sections.42.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.222.updatedAt","sections.42.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.223.createdAt","sections.42.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.223.updatedAt","sections.42.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.224.createdAt","sections.42.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.224.updatedAt","sections.42.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.225.createdAt","sections.42.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.225.updatedAt","sections.42.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.226.createdAt","sections.42.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.226.updatedAt","sections.43.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.227.createdAt","sections.43.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.227.updatedAt","sections.43.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.228.createdAt","sections.43.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.228.updatedAt","sections.43.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.229.createdAt","sections.43.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.229.updatedAt","sections.43.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.230.createdAt","sections.43.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.230.updatedAt","sections.43.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.231.createdAt","sections.43.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.231.updatedAt","sections.43.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.232.createdAt","sections.43.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.232.updatedAt","sections.43.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.233.createdAt","sections.43.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.233.updatedAt","sections.43.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.234.createdAt","sections.43.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.234.updatedAt","sections.44.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.235.createdAt","sections.44.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.235.updatedAt","sections.44.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.236.createdAt","sections.44.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.236.updatedAt","sections.44.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.237.createdAt","sections.44.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.237.updatedAt","sections.44.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.238.createdAt","sections.44.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.238.updatedAt","sections.44.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.239.createdAt","sections.44.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.239.updatedAt","sections.44.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.240.createdAt","sections.44.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.240.updatedAt","sections.44.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.241.createdAt","sections.44.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.241.updatedAt","sections.44.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.242.createdAt","sections.44.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.242.updatedAt","sections.45.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.243.createdAt","sections.45.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.243.updatedAt","sections.45.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.244.createdAt","sections.45.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.244.updatedAt","sections.45.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.245.createdAt","sections.45.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.245.updatedAt","sections.45.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.246.createdAt","sections.45.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.246.updatedAt","sections.45.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.247.createdAt","sections.45.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.247.updatedAt","sections.45.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.248.createdAt","sections.45.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.248.updatedAt","sections.45.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.249.createdAt","sections.45.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.249.updatedAt","sections.46.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.250.createdAt","sections.46.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.250.updatedAt","sections.47.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.251.createdAt","sections.47.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.251.updatedAt","sections.47.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.252.createdAt","sections.47.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.252.updatedAt","sections.47.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.253.createdAt","sections.47.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.253.updatedAt","sections.47.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.254.createdAt","sections.47.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.254.updatedAt","sections.47.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.255.createdAt","sections.47.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.255.updatedAt","sections.48.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.256.createdAt","sections.48.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.256.updatedAt","sections.49.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.257.createdAt","sections.49.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.257.updatedAt","sections.49.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.258.createdAt","sections.49.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.258.updatedAt","sections.49.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.259.createdAt","sections.49.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.259.updatedAt","sections.49.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.260.createdAt","sections.49.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.260.updatedAt","sections.49.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.261.createdAt","sections.49.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.261.updatedAt","sections.50.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.262.createdAt","sections.50.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.262.updatedAt","sections.51.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.263.createdAt","sections.51.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.263.updatedAt","sections.51.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.264.createdAt","sections.51.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.264.updatedAt","sections.51.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.265.createdAt","sections.51.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.265.updatedAt","sections.51.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.266.createdAt","sections.51.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.266.updatedAt","sections.51.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.267.createdAt","sections.51.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.267.updatedAt","sections.52.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.268.createdAt","sections.52.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.268.updatedAt","sections.52.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.269.createdAt","sections.52.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.269.updatedAt","sections.52.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.270.createdAt","sections.52.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.270.updatedAt","sections.52.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.271.createdAt","sections.52.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.271.updatedAt","sections.52.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.272.createdAt","sections.52.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.272.updatedAt","sections.52.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.273.createdAt","sections.52.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.273.updatedAt","sections.52.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.274.createdAt","sections.52.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.274.updatedAt","sections.52.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.275.createdAt","sections.52.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.275.updatedAt","sections.53.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.276.createdAt","sections.53.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.276.updatedAt","sections.53.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.277.createdAt","sections.53.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.277.updatedAt","sections.53.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.278.createdAt","sections.53.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.278.updatedAt","sections.53.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.279.createdAt","sections.53.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.279.updatedAt","sections.53.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.280.createdAt","sections.53.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.280.updatedAt","sections.53.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.281.createdAt","sections.53.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.281.updatedAt","sections.54.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.282.createdAt","sections.54.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.282.updatedAt","sections.54.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.283.createdAt","sections.54.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.283.updatedAt","sections.54.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.284.createdAt","sections.54.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.284.updatedAt","sections.54.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.285.createdAt","sections.54.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.285.updatedAt","sections.54.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.286.createdAt","sections.54.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.286.updatedAt","sections.54.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.287.createdAt","sections.54.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.287.updatedAt","sections.55.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.288.createdAt","sections.55.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.288.updatedAt","sections.55.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.289.createdAt","sections.55.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.289.updatedAt","sections.55.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.290.createdAt","sections.55.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.290.updatedAt","sections.55.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.291.createdAt","sections.55.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.291.updatedAt","sections.55.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.292.createdAt","sections.55.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.292.updatedAt","sections.55.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.293.createdAt","sections.55.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.293.updatedAt","sections.55.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.294.createdAt","sections.55.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.294.updatedAt","sections.55.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.295.createdAt","sections.55.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.295.updatedAt","sections.56.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.296.createdAt","sections.56.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.296.updatedAt","sections.56.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.297.createdAt","sections.56.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.297.updatedAt","sections.56.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.298.createdAt","sections.56.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.298.updatedAt","sections.56.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.299.createdAt","sections.56.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.299.updatedAt","sections.56.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.300.createdAt","sections.56.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.300.updatedAt","sections.56.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.301.createdAt","sections.56.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.301.updatedAt","sections.57.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.302.createdAt","sections.57.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.302.updatedAt","sections.58.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.303.createdAt","sections.58.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.303.updatedAt","sections.58.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.304.createdAt","sections.58.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.304.updatedAt","sections.58.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.305.createdAt","sections.58.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.305.updatedAt","sections.58.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.306.createdAt","sections.58.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.306.updatedAt","sections.58.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.307.createdAt","sections.58.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.307.updatedAt","sections.59.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.308.createdAt","sections.59.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.308.updatedAt","sections.59.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.309.createdAt","sections.59.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.309.updatedAt","sections.59.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.310.createdAt","sections.59.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.310.updatedAt","sections.59.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.311.createdAt","sections.59.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.311.updatedAt","sections.59.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.312.createdAt","sections.59.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.312.updatedAt","sections.59.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.313.createdAt","sections.59.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.313.updatedAt","sections.60.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.314.createdAt","sections.60.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.314.updatedAt","sections.60.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.315.createdAt","sections.60.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.315.updatedAt","sections.60.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.316.createdAt","sections.60.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.316.updatedAt","sections.60.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.317.createdAt","sections.60.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.317.updatedAt","sections.60.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.318.createdAt","sections.60.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.318.updatedAt","sections.60.modules.5.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.319.createdAt","sections.60.modules.5.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.319.updatedAt","sections.60.modules.6.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.320.createdAt","sections.60.modules.6.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.320.updatedAt","sections.60.modules.7.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.321.createdAt","sections.60.modules.7.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.321.updatedAt","sections.60.modules.8.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.322.createdAt","sections.60.modules.8.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.322.updatedAt","sections.61.modules.0.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.323.createdAt","sections.61.modules.0.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.323.updatedAt","sections.61.modules.1.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.324.createdAt","sections.61.modules.1.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.324.updatedAt","sections.61.modules.2.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.325.createdAt","sections.61.modules.2.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.325.updatedAt","sections.61.modules.3.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.326.createdAt","sections.61.modules.3.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.326.updatedAt","sections.61.modules.4.createdAt":"$45:props:state:queries:0:state:data:meta:values:modules.327.createdAt","sections.61.modules.4.updatedAt":"$45:props:state:queries:0:state:data:meta:values:modules.327.updatedAt"},"referentialEqualities":{"modules.0":["sections.0.modules.0"],"modules.1":["sections.0.modules.1"],"modules.2":["sections.0.modules.2"],"modules.3":["sections.0.modules.3"],"modules.4":["sections.1.modules.0"],"modules.5":["sections.1.modules.1"],"modules.6":["sections.1.modules.2"],"modules.7":["sections.2.modules.0"],"modules.8":["sections.2.modules.1"],"modules.9":["sections.2.modules.2"],"modules.10":["sections.2.modules.3"],"modules.11":["sections.2.modules.4"],"modules.12":["sections.2.modules.5"],"modules.13":["sections.2.modules.6"],"modules.14":["sections.2.modules.7"],"modules.15":["sections.3.modules.0"],"modules.16":["sections.3.modules.1"],"modules.17":["sections.3.modules.2"],"modules.18":["sections.3.modules.3"],"modules.19":["sections.3.modules.4"],"modules.20":["sections.4.modules.0"],"modules.21":["sections.4.modules.1"],"modules.22":["sections.4.modules.2"],"modules.23":["sections.4.modules.3"],"modules.24":["sections.4.modules.4"],"modules.25":["sections.4.modules.5"],"modules.26":["sections.4.modules.6"],"modules.27":["sections.4.modules.7"],"modules.28":["sections.4.modules.8"],"modules.29":["sections.5.modules.0"],"modules.30":["sections.5.modules.1"],"modules.31":["sections.5.modules.2"],"modules.32":["sections.5.modules.3"],"modules.33":["sections.5.modules.4"],"modules.34":["sections.5.modules.5"],"modules.35":["sections.5.modules.6"],"modules.36":["sections.5.modules.7"],"modules.37":["sections.6.modules.0"],"modules.38":["sections.6.modules.1"],"modules.39":["sections.6.modules.2"],"modules.40":["sections.6.modules.3"],"modules.41":["sections.6.modules.4"],"modules.42":["sections.6.modules.5"],"modules.43":["sections.7.modules.0"],"modules.44":["sections.7.modules.1"],"modules.45":["sections.8.modules.0"],"modules.46":["sections.8.modules.1"],"modules.47":["sections.8.modules.2"],"modules.48":["sections.8.modules.3"],"modules.49":["sections.8.modules.4"],"modules.50":["sections.8.modules.5"],"modules.51":["sections.8.modules.6"],"modules.52":["sections.9.modules.0"],"modules.53":["sections.9.modules.1"],"modules.54":["sections.9.modules.2"],"modules.55":["sections.9.modules.3"],"modules.56":["sections.9.modules.4"],"modules.57":["sections.9.modules.5"],"modules.58":["sections.10.modules.0"],"modules.59":["sections.10.modules.1"],"modules.60":["sections.11.modules.0"],"modules.61":["sections.11.modules.1"],"modules.62":["sections.11.modules.2"],"modules.63":["sections.11.modules.3"],"modules.64":["sections.11.modules.4"],"modules.65":["sections.12.modules.0"],"modules.66":["sections.12.modules.1"],"modules.67":["sections.12.modules.2"],"modules.68":["sections.12.modules.3"],"modules.69":["sections.12.modules.4"],"modules.70":["sections.12.modules.5"],"modules.71":["sections.13.modules.0"],"modules.72":["sections.14.modules.0"],"modules.73":["sections.14.modules.1"],"modules.74":["sections.14.modules.2"],"modules.75":["sections.14.modules.3"],"modules.76":["sections.14.modules.4"],"modules.77":["sections.14.modules.5"],"modules.78":["sections.14.modules.6"],"modules.79":["sections.14.modules.7"],"modules.80":["sections.15.modules.0"],"modules.81":["sections.15.modules.1"],"modules.82":["sections.15.modules.2"],"modules.83":["sections.15.modules.3"],"modules.84":["sections.15.modules.4"],"modules.85":["sections.15.modules.5"],"modules.86":["sections.15.modules.6"],"modules.87":["sections.16.modules.0"],"modules.88":["sections.16.modules.1"],"modules.89":["sections.16.modules.2"],"modules.90":["sections.16.modules.3"],"modules.91":["sections.16.modules.4"],"modules.92":["sections.16.modules.5"],"modules.93":["sections.17.modules.0"],"modules.94":["sections.17.modules.1"],"modules.95":["sections.18.modules.0"],"modules.96":["sections.18.modules.1"],"modules.97":["sections.18.modules.2"],"modules.98":["sections.18.modules.3"],"modules.99":["sections.19.modules.0"],"modules.100":["sections.19.modules.1"],"modules.101":["sections.19.modules.2"],"modules.102":["sections.19.modules.3"],"modules.103":["sections.19.modules.4"],"modules.104":["sections.19.modules.5"],"modules.105":["sections.19.modules.6"],"modules.106":["sections.19.modules.7"],"modules.107":["sections.20.modules.0"],"modules.108":["sections.20.modules.1"],"modules.109":["sections.20.modules.2"],"modules.110":["sections.21.modules.0"],"modules.111":["sections.21.modules.1"],"modules.112":["sections.21.modules.2"],"modules.113":["sections.22.modules.0"],"modules.114":["sections.22.modules.1"],"modules.115":["sections.22.modules.2"],"modules.116":["sections.22.modules.3"],"modules.117":["sections.22.modules.4"],"modules.118":["sections.22.modules.5"],"modules.119":["sections.23.modules.0"],"modules.120":["sections.23.modules.1"],"modules.121":["sections.23.modules.2"],"modules.122":["sections.23.modules.3"],"modules.123":["sections.23.modules.4"],"modules.124":["sections.23.modules.5"],"modules.125":["sections.24.modules.0"],"modules.126":["sections.24.modules.1"],"modules.127":["sections.24.modules.2"],"modules.128":["sections.24.modules.3"],"modules.129":["sections.24.modules.4"],"modules.130":["sections.24.modules.5"],"modules.131":["sections.24.modules.6"],"modules.132":["sections.25.modules.0"],"modules.133":["sections.25.modules.1"],"modules.134":["sections.25.modules.2"],"modules.135":["sections.26.modules.0"],"modules.136":["sections.26.modules.1"],"modules.137":["sections.26.modules.2"],"modules.138":["sections.26.modules.3"],"modules.139":["sections.27.modules.0"],"modules.140":["sections.27.modules.1"],"modules.141":["sections.27.modules.2"],"modules.142":["sections.27.modules.3"],"modules.143":["sections.27.modules.4"],"modules.144":["sections.27.modules.5"],"modules.145":["sections.28.modules.0"],"modules.146":["sections.28.modules.1"],"modules.147":["sections.28.modules.2"],"modules.148":["sections.28.modules.3"],"modules.149":["sections.28.modules.4"],"modules.150":["sections.28.modules.5"],"modules.151":["sections.28.modules.6"],"modules.152":["sections.28.modules.7"],"modules.153":["sections.29.modules.0"],"modules.154":["sections.29.modules.1"],"modules.155":["sections.29.modules.2"],"modules.156":["sections.30.modules.0"],"modules.157":["sections.30.modules.1"],"modules.158":["sections.30.modules.2"],"modules.159":["sections.31.modules.0"],"modules.160":["sections.31.modules.1"],"modules.161":["sections.31.modules.2"],"modules.162":["sections.31.modules.3"],"modules.163":["sections.31.modules.4"],"modules.164":["sections.31.modules.5"],"modules.165":["sections.31.modules.6"],"modules.166":["sections.31.modules.7"],"modules.167":["sections.32.modules.0"],"modules.168":["sections.32.modules.1"],"modules.169":["sections.32.modules.2"],"modules.170":["sections.32.modules.3"],"modules.171":["sections.32.modules.4"],"modules.172":["sections.32.modules.5"],"modules.173":["sections.33.modules.0"],"modules.174":["sections.33.modules.1"],"modules.175":["sections.33.modules.2"],"modules.176":["sections.34.modules.0"],"modules.177":["sections.34.modules.1"],"modules.178":["sections.34.modules.2"],"modules.179":["sections.35.modules.0"],"modules.180":["sections.35.modules.1"],"modules.181":["sections.35.modules.2"],"modules.182":["sections.35.modules.3"],"modules.183":["sections.35.modules.4"],"modules.184":["sections.35.modules.5"],"modules.185":["sections.35.modules.6"],"modules.186":["sections.36.modules.0"],"modules.187":["sections.36.modules.1"],"modules.188":["sections.36.modules.2"],"modules.189":["sections.36.modules.3"],"modules.190":["sections.36.modules.4"],"modules.191":["sections.36.modules.5"],"modules.192":["sections.37.modules.0"],"modules.193":["sections.37.modules.1"],"modules.194":["sections.37.modules.2"],"modules.195":["sections.37.modules.3"],"modules.196":["sections.37.modules.4"],"modules.197":["sections.37.modules.5"],"modules.198":["sections.38.modules.0"],"modules.199":["sections.38.modules.1"],"modules.200":["sections.38.modules.2"],"modules.201":["sections.38.modules.3"],"modules.202":["sections.38.modules.4"],"modules.203":["sections.38.modules.5"],"modules.204":["sections.39.modules.0"],"modules.205":["sections.39.modules.1"],"modules.206":["sections.39.modules.2"],"modules.207":["sections.39.modules.3"],"modules.208":["sections.39.modules.4"],"modules.209":["sections.39.modules.5"],"modules.210":["sections.39.modules.6"],"modules.211":["sections.39.modules.7"],"modules.212":["sections.40.modules.0"],"modules.213":["sections.40.modules.1"],"modules.214":["sections.40.modules.2"],"modules.215":["sections.40.modules.3"],"modules.216":["sections.41.modules.0"],"modules.217":["sections.41.modules.1"],"modules.218":["sections.41.modules.2"],"modules.219":["sections.41.modules.3"],"modules.220":["sections.41.modules.4"],"modules.221":["sections.42.modules.0"],"modules.222":["sections.42.modules.1"],"modules.223":["sections.42.modules.2"],"modules.224":["sections.42.modules.3"],"modules.225":["sections.42.modules.4"],"modules.226":["sections.42.modules.5"],"modules.227":["sections.43.modules.0"],"modules.228":["sections.43.modules.1"],"modules.229":["sections.43.modules.2"],"modules.230":["sections.43.modules.3"],"modules.231":["sections.43.modules.4"],"modules.232":["sections.43.modules.5"],"modules.233":["sections.43.modules.6"],"modules.234":["sections.43.modules.7"],"modules.235":["sections.44.modules.0"],"modules.236":["sections.44.modules.1"],"modules.237":["sections.44.modules.2"],"modules.238":["sections.44.modules.3"],"modules.239":["sections.44.modules.4"],"modules.240":["sections.44.modules.5"],"modules.241":["sections.44.modules.6"],"modules.242":["sections.44.modules.7"],"modules.243":["sections.45.modules.0"],"modules.244":["sections.45.modules.1"],"modules.245":["sections.45.modules.2"],"modules.246":["sections.45.modules.3"],"modules.247":["sections.45.modules.4"],"modules.248":["sections.45.modules.5"],"modules.249":["sections.45.modules.6"],"modules.250":["sections.46.modules.0"],"modules.251":["sections.47.modules.0"],"modules.252":["sections.47.modules.1"],"modules.253":["sections.47.modules.2"],"modules.254":["sections.47.modules.3"],"modules.255":["sections.47.modules.4"],"modules.256":["sections.48.modules.0"],"modules.257":["sections.49.modules.0"],"modules.258":["sections.49.modules.1"],"modules.259":["sections.49.modules.2"],"modules.260":["sections.49.modules.3"],"modules.261":["sections.49.modules.4"],"modules.262":["sections.50.modules.0"],"modules.263":["sections.51.modules.0"],"modules.264":["sections.51.modules.1"],"modules.265":["sections.51.modules.2"],"modules.266":["sections.51.modules.3"],"modules.267":["sections.51.modules.4"],"modules.268":["sections.52.modules.0"],"modules.269":["sections.52.modules.1"],"modules.270":["sections.52.modules.2"],"modules.271":["sections.52.modules.3"],"modules.272":["sections.52.modules.4"],"modules.273":["sections.52.modules.5"],"modules.274":["sections.52.modules.6"],"modules.275":["sections.52.modules.7"],"modules.276":["sections.53.modules.0"],"modules.277":["sections.53.modules.1"],"modules.278":["sections.53.modules.2"],"modules.279":["sections.53.modules.3"],"modules.280":["sections.53.modules.4"],"modules.281":["sections.53.modules.5"],"modules.282":["sections.54.modules.0"],"modules.283":["sections.54.modules.1"],"modules.284":["sections.54.modules.2"],"modules.285":["sections.54.modules.3"],"modules.286":["sections.54.modules.4"],"modules.287":["sections.54.modules.5"],"modules.288":["sections.55.modules.0"],"modules.289":["sections.55.modules.1"],"modules.290":["sections.55.modules.2"],"modules.291":["sections.55.modules.3"],"modules.292":["sections.55.modules.4"],"modules.293":["sections.55.modules.5"],"modules.294":["sections.55.modules.6"],"modules.295":["sections.55.modules.7"],"modules.296":["sections.56.modules.0"],"modules.297":["sections.56.modules.1"],"modules.298":["sections.56.modules.2"],"modules.299":["sections.56.modules.3"],"modules.300":["sections.56.modules.4"],"modules.301":["sections.56.modules.5"],"modules.302":["sections.57.modules.0"],"modules.303":["sections.58.modules.0"],"modules.304":["sections.58.modules.1"],"modules.305":["sections.58.modules.2"],"modules.306":["sections.58.modules.3"],"modules.307":["sections.58.modules.4"],"modules.308":["sections.59.modules.0"],"modules.309":["sections.59.modules.1"],"modules.310":["sections.59.modules.2"],"modules.311":["sections.59.modules.3"],"modules.312":["sections.59.modules.4"],"modules.313":["sections.59.modules.5"],"modules.314":["sections.60.modules.0"],"modules.315":["sections.60.modules.1"],"modules.316":["sections.60.modules.2"],"modules.317":["sections.60.modules.3"],"modules.318":["sections.60.modules.4"],"modules.319":["sections.60.modules.5"],"modules.320":["sections.60.modules.6"],"modules.321":["sections.60.modules.7"],"modules.322":["sections.60.modules.8"],"modules.323":["sections.61.modules.0"],"modules.324":["sections.61.modules.1"],"modules.325":["sections.61.modules.2"],"modules.326":["sections.61.modules.3"],"modules.327":["sections.61.modules.4"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792296755642,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningPaths","getBySlug"],{"input":{"slug":"machine-learning"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getBySlug\"],{\"input\":{\"slug\":\"machine-learning\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755959,"state":{"data":{"json":{"path":{"archivedAt":null,"id":"seed_b2b87dff658cc600bb5313282ab109da","slug":"machine-learning","title":"Machine Learning","description":"Learn the mathematical and engineering foundations of machine learning, explore executable visualizers, implement algorithms, and validate dataset releases in repository exercises.","image":null,"color":"#8B5CF6","keyTopics":["Linear Algebra & Calculus","Probability & Statistics","Supervised Learning","Unsupervised Learning","Neural Networks","Deep Learning","Reinforcement Learning","ML Engineering"],"estimatedHours":252.3,"problemCount":39,"visualizerCount":12,"quizCount":0,"learningCount":277,"difficulty":"ADVANCED","idealFor":["Data scientists and ML engineers","Software engineers transitioning to ML","Researchers and academics","Anyone wanting to understand AI/ML deeply"],"prerequisites":["Basic programming (Python preferred)","High school math (algebra, basic calculus)","Comfort with mathematical notation"],"skillsGained":["Mathematical foundations for ML","Classical ML algorithms","Deep learning architectures","Model training and evaluation","Feature engineering","ML system design"],"outcomes":["Build and train ML models from scratch","Understand the math behind algorithms","Apply appropriate algorithms to problems","Design neural network architectures","Deploy and maintain ML systems","Stay current with ML research"],"overview":"This comprehensive learning path takes you from mathematical foundations to cutting-edge ML techniques, preparing you for ML engineering and research roles.\n\n**What you'll learn:**\n• Mathematical foundations (linear algebra, calculus, probability)\n• Statistical learning theory\n• Regression and classification algorithms\n• Ensemble methods (Random Forests, XGBoost)\n• Model evaluation and hyperparameter tuning\n• Unsupervised learning (clustering, dimensionality reduction)\n• Bayesian methods and probabilistic models\n• Neural networks and deep learning\n• CNNs, RNNs, and Transformers\n• Generative models and reinforcement learning\n• ML engineering and deployment\n\nEach topic includes detailed explanations with mathematical rigor.","createdAt":"2026-10-15T17:47:45.111Z","updatedAt":"2026-10-17T16:28:34.124Z","modules":[{"id":"seed_458ccce80e8010395b21b166cb7157a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"What Is Machine Learning?","order":1,"contentType":"LEARNING","contentSlug":"what-is-machine-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a8d2537620b279c45bcc44b3c7e06647","title":"Formal Definition of Machine Learning","order":0},{"id":"seed_42ae00d692e83326eafe93c64c40bb17","title":"Learning from Data","order":1},{"id":"seed_a3a44c76b32adb410dc3fb4c6ffa340c","title":"Machine Learning vs Traditional Programming","order":2},{"id":"seed_1ea4f1f817746555c2ae12ee6c01297f","title":"Types of Learning: Supervised, Unsupervised, Reinforcement","order":3},{"id":"seed_779a6d64b4c2c855df560e5c96bea216","title":"Historical Perspective: The Evolution of Machine Learning","order":4}]},{"id":"seed_713656282288e703aa91193ff6640a1e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"The Machine Learning Pipeline","order":2,"contentType":"LEARNING","contentSlug":"the-ml-pipeline","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7b5489128cd6e7e05f3bed002cf61afb","title":"Problem Formulation","order":0},{"id":"seed_182a0a13f707287352f2006ac28a03a5","title":"Data Collection and Preparation","order":1},{"id":"seed_d345261d68ef95a3fd3a6c42a99479a0","title":"Feature Engineering","order":2},{"id":"seed_c41fad33c5bc70e85bf227cc118c53ac","title":"Model Selection and Training","order":3},{"id":"seed_a4db3b4deb24585d8eb0a84c1dbf9a1f","title":"Evaluation and Deployment","order":4}]},{"id":"seed_265702d858afc6a50a187b96538c09bf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Key Concepts and Terminology","order":3,"contentType":"LEARNING","contentSlug":"key-concepts-and-terminology","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cf917ae40b837629edc01d8626203d0b","title":"Features and Labels: The Language of Machine Learning Data","order":0},{"id":"seed_4b60b97fce71866f2f91c1bd6e21df4a","title":"Training, Validation, and Test Sets: The Foundation of Model Evaluation","order":1},{"id":"seed_1d2da31986bb79ef8b695328f0141d9a","title":"The Hypothesis Space: The Universe of Possible Models","order":2},{"id":"seed_34c88496a21b167b120c52098ade1ec9","title":"Loss Functions: Quantifying Model Error","order":3},{"id":"seed_cabd0a1a0688b3a963731496f4f1439b","title":"Generalization: The Ultimate Goal of Machine Learning","order":4}]},{"id":"seed_3f6d09c97b1e031f237b2abc340e3cbd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Machine Learning","sectionOrder":1,"title":"Machine Learning Success Factors","order":4,"contentType":"LEARNING","contentSlug":"ml-success-factors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_892fd230224ca5b877b22855e92c805a","title":"Data Quality and Quantity","order":0},{"id":"seed_e49b70b428629e50ef94ddf0aa1940b8","title":"Feature Representation","order":1},{"id":"seed_e1310a68c40d8c3fab7d75ca921d97e6","title":"Algorithm Selection","order":2},{"id":"seed_488bd94cf8d4015a6a3b18c3b8c6daca","title":"Computational Resources","order":3},{"id":"seed_abe193f4d7a82a578939e9a0ae77b0e5","title":"Domain Expertise","order":4}]},{"id":"seed_f1d78b0dba00cdd6d7891c54667b63c0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"What Is Machine Learning: The Foundation of Intelligent Systems","order":1,"contentType":"LEARNING","contentSlug":"what-is-ml","description":null,"estimatedMins":20,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_88cf405cd795a6a19169ce19aae9784d","title":"Formal Definition of Machine Learning","order":0},{"id":"seed_0b8431fdcbfce40e28ed571c25eb4faf","title":"Learning From Data","order":1},{"id":"seed_cadc325f21de5cb0d33f73a9130a21c6","title":"ML vs Traditional Programming","order":2},{"id":"seed_3136c4817ec4ac63d64b5ded9f84bbb8","title":"Types of Learning","order":3},{"id":"seed_a0add1ca687de3c16d9eb259e942a117","title":"Historical Perspective","order":4}]},{"id":"seed_3cd2b6f1422ffede96de1e0597239aa9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"The Machine Learning Landscape","order":2,"contentType":"LEARNING","contentSlug":"ml-landscape","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_5a7337716a8243ae6ac163168a9358a7","title":"Regression Problems","order":0},{"id":"seed_fab408ffe1d63967fa4033555fc3e147","title":"Classification Problems","order":1},{"id":"seed_1b70dfd5a9794b9e726f1932a75f534b","title":"Clustering Problems","order":2},{"id":"seed_68607512cc45e789ed2d9daf60162566","title":"Structured Prediction","order":3},{"id":"seed_bb20c6cdbd72b02b6b2ff2f86b708fbb","title":"Generative Modeling","order":4}]},{"id":"seed_be70ea380f07d6ea7e2bdcfc3143d40a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 1: Introduction To Ml","sectionOrder":2,"title":"When to Use Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"when-to-use-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a76436dc42aca300589c343e150664c7","title":"ML vs Rule-Based Systems","order":0},{"id":"seed_99a406a2e58c0bca81a9eb883bae835a","title":"Data Requirements for Machine Learning","order":1},{"id":"seed_1a1f40adc5952a5033af70a118210765","title":"Problem Complexity Assessment","order":2},{"id":"seed_1b0c603ce8398a59ea0709b89386c355","title":"Interpretability Needs","order":3},{"id":"seed_45439963ca15ec8736e63486ff7a18e4","title":"Cost-Benefit Analysis for ML Projects","order":4}]},{"id":"seed_0160e8621c11b759ae3cc62ac74840f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Vectors and Vector Spaces: The Language of Machine Learning","order":1,"contentType":"LEARNING","contentSlug":"vectors-and-vector-spaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b249a75d4e7cf423557152dffdc02360","title":"Vectors as Ordered Lists","order":0},{"id":"seed_da63346642aa9a8e87f4b1393eb6091c","title":"Vector Operations","order":1},{"id":"seed_fe168a730e8cd78653fea1585f6b698f","title":"Linear Combinations","order":2},{"id":"seed_cd38d02c2681259630844c7a642def7b","title":"Span and Linear Independence","order":3},{"id":"seed_f4826e48bdca2a36fbaaaeabfffa900d","title":"Vector Spaces and Subspaces","order":4}]},{"id":"seed_62c3a9c331653ca339db3e3f28ff8baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Eigenvalues and Eigenvectors","order":2,"contentType":"LEARNING","contentSlug":"eigenvalues-and-eigenvectors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9f803806fff64c49ce44cc34930d91a8","title":"Eigenvalue Problem Definition","order":0},{"id":"seed_e431672dc58f8e16c08fa71e67f0c57b","title":"Geometric Interpretation","order":1},{"id":"seed_273883cd454631f7960194d9c6d25483","title":"Computing Eigenvalues","order":2},{"id":"seed_da8891e32b2170ca8b04aab194d1c4c2","title":"Diagonalization","order":3},{"id":"seed_b2ebf8e0471b83bd5e125f0d5854eff9","title":"Applications in ML (PCA, PageRank)","order":4}]},{"id":"seed_79a3d1325d227a51f27b9ef5234cd9a9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Matrix Decompositions","order":3,"contentType":"LEARNING","contentSlug":"matrix-decompositions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4f98567a3dc4b270d751df8319ebfd44","title":"Singular Value Decomposition (SVD)","order":0},{"id":"seed_e1bbecc3b6b08c0f06fa0ea425defc1d","title":"Eigendecomposition","order":1},{"id":"seed_f67c18eed67ce194cd29e83762ded3ea","title":"QR Decomposition","order":2},{"id":"seed_8e7d4c7b10ee5a045b407352347e3427","title":"Cholesky Decomposition","order":3},{"id":"seed_de9d56fa647c98ac1ee92c884fbf2ddd","title":"Applications in Machine Learning","order":4}]},{"id":"seed_1a7e06e15bba9acf14df3d33c9f77125","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Projections and Least Squares","order":4,"contentType":"LEARNING","contentSlug":"projections-and-least-squares","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2086b527c96bf555f32000d2e1736b82","title":"Orthogonal Projection","order":0},{"id":"seed_2104be958fbffc03f924f6fa378a2363","title":"Projection Matrices","order":1},{"id":"seed_ed1e375898cef23ccfb7f4b445b47955","title":"Normal Equations","order":2},{"id":"seed_8b41e076eab71545daec0e9b603c64b5","title":"Geometric View of Least Squares","order":3},{"id":"seed_e83e52dbb1beae0a6e5beed6d2f75a16","title":"Pseudoinverse","order":4}]},{"id":"seed_fe00df3a3ce80f8b8080b9d940134165","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":5,"contentType":"VISUALIZER","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_95423ea3af30d0a2a29f5e934888f366","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Linear Transformation of a Vector","order":6,"contentType":"PROBLEM","contentSlug":"linear-transformation-vector","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_58253712c8015db94782d197ddc36627","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Pairwise Matrix Product Computation","order":7,"contentType":"PROBLEM","contentSlug":"matrix-product-multiplication","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_22c7972151c0c5ecdeb281eb35ce19d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Linear Algebra","sectionOrder":3,"title":"Computing the Multiplicative Inverse of a 2×2 Matrix","order":8,"contentType":"PROBLEM","contentSlug":"matrix-inverse-2x2","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_83ea72a6e731d4dbcd355861ca589959","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Matrices and Linear Transformations","order":1,"contentType":"LEARNING","contentSlug":"matrices-and-linear-transformations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4cd103eb060cc85b6f01d1e475bb1ab2","title":"Matrices as Linear Transformations","order":0},{"id":"seed_1adf7f5643518f2b456f4ab347e22784","title":"Matrix Operations","order":1},{"id":"seed_45029d7b2fca7b035cdc9d0f7186e0c0","title":"Matrix Multiplication Interpretation","order":2},{"id":"seed_5eba53ce1cf3188d0715abc741f997ac","title":"Rank and Nullity","order":3},{"id":"seed_296937a51259ce1a485f70f00a71586a","title":"Inverse Matrices","order":4}]},{"id":"seed_e52e478149e70c47f5ecdb61ff144516","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Norms and Distance Metrics","order":2,"contentType":"LEARNING","contentSlug":"norms-and-distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d4b9bc83f81877a3029c2ddc41bcee52","title":"Vector Norms (L1, L2, L∞)","order":0},{"id":"seed_237deae88d3c596844182f012a8a8121","title":"Matrix Norms","order":1},{"id":"seed_5b5f62c9ec9f8169d10958fd360b2cb7","title":"Distance Metrics","order":2},{"id":"seed_d7633facee2df16feee862ce5b47f614","title":"Similarity Measures","order":3},{"id":"seed_499ef81eebb4d9d03fe2a927e8f955cd","title":"Norm Regularization","order":4}]},{"id":"seed_1b167b3e9032b1f5cd4490b4f4ef40b2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":3,"contentType":"VISUALIZER","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_e4f70753ddb0e7daa22b01cddae4d0cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Compute Arithmetic Mean Along Matrix Axis","order":4,"contentType":"PROBLEM","contentSlug":"matrix-axis-mean-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_d267450d674287a1391f487fe97633fa","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 2: Mathematical Foundations Linear Algebra","sectionOrder":4,"title":"Feature Covariance Computation","order":5,"contentType":"PROBLEM","contentSlug":"feature-covariance-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_715f767142a8ca5f68ecbe95188b9171","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Differential Calculus Review","order":1,"contentType":"LEARNING","contentSlug":"differential-calculus-review","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_c9e167b399bfe6e2a2cd05b38ea7a6a0","title":"Derivatives and Partial Derivatives","order":0},{"id":"seed_ee450489f30583b22470ac414f0db899","title":"The Chain Rule","order":1},{"id":"seed_407f9675b96cb53355b75e1049d8e1b1","title":"Gradients","order":2},{"id":"seed_81a8ec988aa20438259f708dc5a6e268","title":"Directional Derivatives","order":3},{"id":"seed_2cb1c4dc90c61ba0d261e59125fca51c","title":"The Hessian Matrix","order":4}]},{"id":"seed_136b74dda11c6ede585436811675d040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Multivariate Calculus","order":2,"contentType":"LEARNING","contentSlug":"multivariate-calculus","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_ce27e0d0737553aa3dd2a5fec27a5b10","title":"Multivariable Functions — Foundations and Representations","order":0},{"id":"seed_809a050cbe29c7a691ddbdd9d856b24d","title":"Gradient Vectors — The Direction of Steepest Ascent","order":1},{"id":"seed_8b37c4d7357d63a1542f6e3efb093050","title":"Jacobian Matrices — Generalizing Derivatives to Vector Functions","order":2},{"id":"seed_3f36abe3c854e4be0ebf1f523d56d622","title":"Taylor Series Expansion — Approximating Functions Locally","order":3},{"id":"seed_b255469361018065decdb144e18ca48f","title":"Second-Order Approximations and the Hessian Matrix","order":4}]},{"id":"seed_5d93b2deb9a5171452b8f10b0927266b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Convex Optimization","order":3,"contentType":"LEARNING","contentSlug":"convex-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a62534e72db72ef29bb3b59299dd0d6e","title":"Convex Sets and Functions","order":0},{"id":"seed_87be7e753b48c155171285b56d265c0d","title":"Local vs Global Optima","order":1},{"id":"seed_5af7ee4b4f9e80296cdd65e3f42b5576","title":"First-Order Conditions","order":2},{"id":"seed_625c430c888f1228c222fc4b857a2e3f","title":"Second-Order Conditions","order":3},{"id":"seed_0b290cf8770e21f059694963f68426c5","title":"Convexity in ML","order":4}]},{"id":"seed_c17e63b9a5e2e3517a5c1a05e1c8687e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Descent","order":4,"contentType":"LEARNING","contentSlug":"gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_c1b8e66156a49e7a736fc4c98ffcfba3","title":"Gradient Descent Algorithm","order":0},{"id":"seed_3c124686ceeae53f663f43f9a98d9ed5","title":"Learning Rate Selection","order":1},{"id":"seed_943b4a2b8c7cbee179caa6129f61c060","title":"Convergence Analysis","order":2},{"id":"seed_2fa7c3ee753285bf9b4985d3d0abd094","title":"Variants: Batch, Stochastic, Mini-batch","order":3},{"id":"seed_e99fb1d5dfbc5d063b5fad188dacb836","title":"Momentum and Acceleration","order":4}]},{"id":"seed_58a6b4d26b778b4a73205b09fd21b0f4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Constrained Optimization","order":5,"contentType":"LEARNING","contentSlug":"constrained-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_6eced085b9f76f74c478283576931600","title":"Lagrange Multipliers","order":0},{"id":"seed_622661cafc93e1cf7e1eee30e406d88f","title":"Karush-Kuhn-Tucker (KKT) Conditions","order":1},{"id":"seed_067090088a74e55e731d3f7c46d73b0d","title":"Duality: The Mirror World of Optimization","order":2},{"id":"seed_dd388937ab241c77e6fd44842642b230","title":"Dual Problem Formulation","order":3},{"id":"seed_cfd432f9c54180224edd043cb827233d","title":"Applications: Support Vector Machines","order":4}]},{"id":"seed_b0bf23bffa2221135e1bfa0877fc16a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Advanced Optimization Methods","order":6,"contentType":"LEARNING","contentSlug":"advanced-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_589a6b98aff004e39ca82a7c1e99bdb6","title":"Newton's Method","order":0},{"id":"seed_096687b06e5533b1372eeb8ae5616c71","title":"Quasi-Newton Methods (BFGS, L-BFGS)","order":1},{"id":"seed_d959397675821e4e63a0cdd5679cef77","title":"Coordinate Descent","order":2},{"id":"seed_c01022a29f9e92d7e5134127d547526b","title":"Proximal Methods","order":3},{"id":"seed_cc611f28262e9e8cb78954194121d39b","title":"Optimization Landscapes","order":4}]},{"id":"seed_87fed16558b28ac02f40835f8205b2eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":7,"contentType":"VISUALIZER","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_1e177e352ea702e7bdeb96bc180c96b0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Composite Function Differentiation via Chain Rule","order":8,"contentType":"PROBLEM","contentSlug":"composite-function-differentiation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_6538cb1ab6830bbd13c96c2518a5bbab","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 3: Calculus Optimization","sectionOrder":5,"title":"Gradient Vector Computation for Scalar Fields","order":9,"contentType":"PROBLEM","contentSlug":"gradient-vector-computation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_73aa88219e07d9fd5f5fb52f60c5bde7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Probability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"probability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d56be98f9a7734451103c90f4f54b9ae","title":"Sample Spaces and Events","order":0},{"id":"seed_4fe8e454d7b60a6389ac49408310cbe7","title":"Axioms of Probability","order":1},{"id":"seed_750aba734dba935aaf9b8606f4b4c2fe","title":"Conditional Probability","order":2},{"id":"seed_0dd8f1b09d7ee81a14fb89122f810455","title":"Independence","order":3},{"id":"seed_954fbbb4fefae33dc24611cf53b5d0a7","title":"Bayes' Theorem","order":4}]},{"id":"seed_501a2bc6ca2690e1bb6027ebab947dbb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Random Variables and Distributions","order":2,"contentType":"LEARNING","contentSlug":"random-variables-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9a96e400f4a0b79e93e447aabee3ac1f","title":"Discrete Random Variables","order":0},{"id":"seed_d4c85b11ec12710bd64bab83c6d97fc1","title":"Continuous Random Variables","order":1},{"id":"seed_db0d600ac28f0bb1b92f18e3ac6d0d25","title":"PMF, PDF, and CDF","order":2},{"id":"seed_fabbdf76198597f92fc3292499152dbe","title":"Expectation and Variance","order":3},{"id":"seed_758b4d58f78986a9ab02372bec41ad41","title":"Moments","order":4}]},{"id":"seed_4bedc7e467feee3964d3f6d9adc5c5a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Common Probability Distributions","order":3,"contentType":"LEARNING","contentSlug":"common-probability-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_0c9a85329763186db1f715343058c80f","title":"Bernoulli and Binomial Distributions","order":0},{"id":"seed_25f719c3273145b70eb2f19c69355475","title":"Gaussian (Normal) Distribution","order":1},{"id":"seed_115fcdefdc6e70187668e21bff7b7484","title":"Poisson Distribution","order":2},{"id":"seed_924e0c15a9b88f21fdd72e29b0d09cc8","title":"Exponential Family of Distributions","order":3},{"id":"seed_aa0b8ec7d40e7b9c2d723080a411f1c6","title":"Multivariate Gaussian Distribution","order":4}]},{"id":"seed_de316035d6a3b7c5c82a9b8d273802de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Joint and Marginal Distributions","order":4,"contentType":"LEARNING","contentSlug":"joint-marginal-distributions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4c687dc6d5f4dd83f7f8ffdd04fb0404","title":"Joint Distributions","order":0},{"id":"seed_2202654b56bf7ce2e1e6358b9b605b80","title":"Marginal Distributions","order":1},{"id":"seed_0221448e7bba3022344ddde383da1de6","title":"Conditional Distributions","order":2},{"id":"seed_d8d5ae5b8ec072dfee02dfb89c2dc130","title":"Covariance and Correlation","order":3},{"id":"seed_24f5f9a8609c4acbddfede295342f551","title":"Independence","order":4}]},{"id":"seed_3e205a6ba092f97e52401620397afc15","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Estimation Theory","order":5,"contentType":"LEARNING","contentSlug":"estimation-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_e3a8b67c5ec75fdffbc77263b10fd3d3","title":"Maximum Likelihood Estimation (MLE)","order":0},{"id":"seed_9d5719ab49afa4ca49341ef7d1bce70b","title":"Maximum A Posteriori (MAP) Estimation","order":1},{"id":"seed_5a2922e603c249fec1cec76f5a36ce9a","title":"Bias and Variance of Estimators","order":2},{"id":"seed_a6103baa2d58d0fc08937d947c6b8289","title":"Consistency of Estimators","order":3},{"id":"seed_af0f2c9c163f136cc39c378528e68501","title":"Efficiency of Estimators","order":4}]},{"id":"seed_9d79f7e518e418caedc3c16ad4567312","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Information Theory for Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"information-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_403e429e44b0906096d7cd03c2f49b0c","title":"Entropy: Quantifying Uncertainty","order":0},{"id":"seed_7299687f9c6cd446106fa16599a6204c","title":"Cross-Entropy: The Language of Classification Loss","order":1},{"id":"seed_f4ca0321720d0706e26a95c980f6bfcd","title":"KL Divergence: Measuring Distributional Difference","order":2},{"id":"seed_205f1efa0dd32e9fd058df697858811d","title":"Mutual Information: Quantifying Shared Information","order":3},{"id":"seed_d7f47bfc8afd583915bff0be3fbd484d","title":"Information Theory in Machine Learning: A Unified View","order":4}]},{"id":"seed_c5e69fdba5ec3502e7ecb9a2729a06be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Population Mean Confidence Estimator","order":7,"contentType":"PROBLEM","contentSlug":"population-mean-confidence-estimator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_5b652c4511070dff688b90df174e04cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 4: Probability Theory","sectionOrder":6,"title":"Independent Samples Mean Comparison","order":8,"contentType":"PROBLEM","contentSlug":"independent-samples-mean-comparison","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_66416329d1070e25819caacd6051f101","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Learning Problem","order":1,"contentType":"LEARNING","contentSlug":"the-learning-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_efd227ef78ab77f88417bcaec01baf26","title":"Formal Learning Framework","order":0},{"id":"seed_e527b2a901d29dadb1057f4f33e118f9","title":"Empirical Risk Minimization","order":1},{"id":"seed_d1100ad43324f51b8abc85f7baa5536a","title":"True Risk vs Empirical Risk","order":2},{"id":"seed_739bc97e30205aa6637cf74635ace69e","title":"Generalization Gap","order":3},{"id":"seed_c10617fbc9750999220fb74d60d5f1f1","title":"No Free Lunch Theorem","order":4}]},{"id":"seed_27b627da90e780a7304764b0313cb5a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"PAC Learning: Probably Approximately Correct Framework","order":2,"contentType":"LEARNING","contentSlug":"pac-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_6442d1525ca0eef60c1763dada8b1a66","title":"Probably Approximately Correct Framework","order":0},{"id":"seed_6623868e6dc7b18f190e099db36a6a8c","title":"Sample Complexity","order":1},{"id":"seed_449f076162b461f08936b9cffc878fc7","title":"PAC-Learnability","order":2},{"id":"seed_0715644b93cd64d3f33704bb008d977d","title":"Finite Hypothesis Classes","order":3},{"id":"seed_d1c50019a25b196405bc95d1712a936e","title":"Computational Complexity","order":4}]},{"id":"seed_91308bda8e0cece0354a33420f6bab7c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"VC Dimension and Model Complexity","order":3,"contentType":"LEARNING","contentSlug":"vc-dimension","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_837b0197b58f58800b6929d82917562c","title":"Shattering: The Foundation of VC Theory","order":0},{"id":"seed_0322860aedd69fdb804fbe7aec5e68e3","title":"VC Dimension: Formal Definition and Properties","order":1},{"id":"seed_15d60f4805b3fc39c9218948a64ceccc","title":"VC Dimension of Linear Classifiers","order":2},{"id":"seed_47246e73be4769f88b5b1c581d4db307","title":"Generalization Bounds from VC Dimension","order":3},{"id":"seed_34b2d48d22ea18b27e951e037b0ba7d3","title":"Structural Risk Minimization","order":4}]},{"id":"seed_74a06a67e984c7f33ea72321e27fe638","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"The Bias-Variance Tradeoff","order":4,"contentType":"LEARNING","contentSlug":"bias-variance-tradeoff","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a1f220cf59d307c9033e2f5989d13063","title":"Bias-Variance Decomposition","order":0},{"id":"seed_dd1180784ccf308f3e00280f30397677","title":"Sources of Error","order":1},{"id":"seed_70bbcb80ed75408b2697ec7516b93221","title":"Model Complexity","order":2},{"id":"seed_e5ad0cc737f80687f8fd4169dafea187","title":"Tradeoff Visualization","order":3},{"id":"seed_3d9b7d2c2d31b7e5826d314f68722c42","title":"Implications for Model Selection","order":4}]},{"id":"seed_6f12c2ff794f5c0a3d28930627f9e9ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Regularization Theory","order":5,"contentType":"LEARNING","contentSlug":"regularization-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b81489822980346014c9e0db33aa22cf","title":"Regularization Motivation","order":0},{"id":"seed_9bc99362d1468932347d98eeafca8f6a","title":"Regularization as Constraint","order":1},{"id":"seed_08658b1c87830994c56a47693372da9d","title":"Regularization as Prior","order":2},{"id":"seed_156e099a21f52dbdc2bd305c8b4fde9c","title":"Effects on Generalization","order":3},{"id":"seed_44a18836eea49bfe7fdafd4c2e52caa8","title":"Common Regularizers","order":4}]},{"id":"seed_8848fbde5a5ec45b7dcbbb6bcd4ab9ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 5: Statistical Learning Theory","sectionOrder":7,"title":"Generalization Bounds: Mathematical Guarantees for Learning","order":6,"contentType":"LEARNING","contentSlug":"generalization-bounds","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2b9e3d28d1c3cb4bac3e0a51fbc5a0f5","title":"The Union Bound: Foundation of Generalization Theory","order":0},{"id":"seed_5d674cff25592aa0c6073f4dee28e6cc","title":"Hoeffding's Inequality: Exponential Concentration for Bounded Variables","order":1},{"id":"seed_21e7c3e6ad7c0f28eb583ae7953ea97b","title":"Rademacher Complexity: Data-Dependent Generalization Bounds","order":2},{"id":"seed_cc23793c711008d6fbe01e8c5fd44633","title":"Margin-Based Bounds: Geometric Structure for Generalization","order":3},{"id":"seed_f07b20cc7fbcef6aaae51aee140f7112","title":"PAC-Bayes Bounds: Bayesian Generalization Theory","order":4}]},{"id":"seed_82fa8bc43938f7a884129594f86d7d81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Regression Diagnostics","order":1,"contentType":"LEARNING","contentSlug":"regression-diagnostics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_2ec0a15230f2da55ced82fc8aad6d13a","title":"Residual Analysis","order":0},{"id":"seed_a96b5be71fe3dc6e59214ac688967b26","title":"Normality Tests","order":1},{"id":"seed_19b6a00db2e39664afebf2509ef30922","title":"Heteroscedasticity","order":2},{"id":"seed_4a4f57ebe5b39237814f4bbe50047178","title":"Influential Points","order":3},{"id":"seed_32e3e905cb78afc0042e4b9c4725c143","title":"Multicollinearity","order":4}]},{"id":"seed_c6e739d9eb19caa6b379150681b5e7cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression","sectionOrder":8,"title":"Computational Considerations in Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"computational-considerations-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cc08b5f3b89ab17cc80cc1ae495a2ad1","title":"Numerical Stability","order":0},{"id":"seed_2e8a100634acba71f9d69d243b21c1ac","title":"QR Decomposition Solution","order":1},{"id":"seed_8f7a6e10bd46099a2b5e6949d6259d4d","title":"Complexity Analysis","order":2},{"id":"seed_d626d357937e0d9615a995e9fb9fb771","title":"Online Updates","order":3},{"id":"seed_01a54def315c67fdbf7d74a929a6037a","title":"Weighted Least Squares","order":4}]},{"id":"seed_829f2ade22abec4e2b345562ce75988e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Simple Linear Regression","order":1,"contentType":"LEARNING","contentSlug":"simple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_97d46af62464a1e775d8b099e3dce5c5","title":"Model Formulation","order":0},{"id":"seed_6632aadcd0a92a39a62c0ddaff84a755","title":"Least Squares Derivation","order":1},{"id":"seed_5302711156dc7225bf7df36434b35477","title":"Geometric Interpretation","order":2},{"id":"seed_b86b4d4971f612f160049abff5b34766","title":"Coefficient Interpretation","order":3},{"id":"seed_f2af5d8fd60deee5e1e01602b4b096b4","title":"Assumptions","order":4}]},{"id":"seed_f2efa2b99627070158e4ef4b79c540a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Multiple Linear Regression","order":2,"contentType":"LEARNING","contentSlug":"multiple-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4447d3d0f9a46eb6fd265ea635835a29","title":"Matrix Formulation","order":0},{"id":"seed_198093b140733c0df22020bf40c907c5","title":"Normal Equations Derivation","order":1},{"id":"seed_d5f53539876c2787304eb4a7338f11a8","title":"OLS Estimator Properties","order":2},{"id":"seed_0ffbfe958196b00c5f6eee50c4f54c17","title":"Geometric Interpretation","order":3},{"id":"seed_84ca7315399fdc1f7552ce87f6386959","title":"Projection Perspective","order":4}]},{"id":"seed_a7bdfcdeda9f1afd7fb7b9fc202d3f81","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Statistical Properties of OLS","order":3,"contentType":"LEARNING","contentSlug":"statistical-properties-of-ols","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_4cf8c398ca23d6bffdfb16838b4c746f","title":"The Gauss-Markov Theorem","order":0},{"id":"seed_0bdee0d7b2b47780c5a6645c25cf6a5d","title":"The BLUE Property and Minimum Variance Estimation","order":1},{"id":"seed_e9cebcb4c43060e8ed5692d74af7bb6c","title":"Variance of OLS Estimates and Standard Errors","order":2},{"id":"seed_67721dbeff81ffbb90ae6357969397e6","title":"Confidence Intervals for Regression Coefficients","order":3},{"id":"seed_28498b0501da539124ab8301abc4ad70","title":"Hypothesis Testing for Regression Coefficients","order":4}]},{"id":"seed_916ec3501e133742dec47a2102539082","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Maximum Likelihood View of Linear Regression","order":4,"contentType":"LEARNING","contentSlug":"maximum-likelihood-view","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_32f27f1b01674466e994bd1e21527370","title":"Gaussian Noise Assumption","order":0},{"id":"seed_8db83899bb21c42622e269e570227e99","title":"Log-Likelihood Derivation","order":1},{"id":"seed_fb879277abb35c208e0cc42a1467e3b7","title":"MLE Solution","order":2},{"id":"seed_061e6610cb74f4ed7f33b7c0590f1d55","title":"Connection to OLS","order":3},{"id":"seed_45512c47296921d0fe3bddb11626af61","title":"Variance Estimation","order":4}]},{"id":"seed_37c09e6af50bedd36be1dc9600378f95","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":5,"contentType":"VISUALIZER","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_1eaf702ff974ccd2ce05bd4f9821f236","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Closed-Form Linear Model Fitting","order":6,"contentType":"PROBLEM","contentSlug":"closed-form-linear-model-fitting","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_ff8d00adbbea598d4881affb39e91b05","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 6: Linear Regression Deep Dive","sectionOrder":9,"title":"Optimizing Linear Model Coefficients via Gradient Descent","order":7,"contentType":"PROBLEM","contentSlug":"gradient-descent-linear-regression","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_d97a7c01ffd0b34a093375e7578d029d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"The Overfitting Problem","order":1,"contentType":"LEARNING","contentSlug":"the-overfitting-problem","description":null,"estimatedMins":40,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_e876e40a3595abeca4651398e0ee17b0","title":"Training vs Test Error","order":0},{"id":"seed_3882ab2d79f353ce2117df7a2b816e1c","title":"High-Dimensional Settings","order":1},{"id":"seed_751af5c393757310899d3b58eedfa5f8","title":"Variance Explosion","order":2},{"id":"seed_17c0aa662cf495807c0daa75fd463f27","title":"Need for Regularization","order":3}]},{"id":"seed_12992e935695a3adf10d331bc32c3762","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Ridge Regression (L2 Regularization)","order":2,"contentType":"LEARNING","contentSlug":"ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b9e9dfe0ae36df514073f9cceecb946b","title":"L2 Penalty Formulation","order":0},{"id":"seed_4758f319a81fc4635807084ab562b006","title":"Closed-Form Solution","order":1},{"id":"seed_b893e733a06e377d936fc13e0ad1b589","title":"Shrinkage Interpretation","order":2},{"id":"seed_f344d9adfe936a8ea7995abfb69ccc75","title":"Bias-Variance Tradeoff","order":3},{"id":"seed_c8252a8fbb7a984d1704e5e753096f16","title":"Selecting Regularization Strength","order":4}]},{"id":"seed_6bb2f7c3adb3a36845ac25fd5e6983ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Lasso Regression (L1 Regularization)","order":3,"contentType":"LEARNING","contentSlug":"lasso-regression-l1","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_3c09683231831d59fa0c1341cf71bd7c","title":"L1 Penalty Formulation","order":0},{"id":"seed_818ba9c95903630e6a47446b7791e730","title":"Sparsity Inducing Property","order":1},{"id":"seed_44a5a072c981a0ae68784263e944f5a5","title":"Geometric Interpretation","order":2},{"id":"seed_05224b981d79da19989db9da16935549","title":"Solution Algorithms (Coordinate Descent)","order":3},{"id":"seed_c73fe0f7506794f407d6467452dbc493","title":"Feature Selection Aspect","order":4}]},{"id":"seed_467edb60b91d6f7d7c4cb891f946f6ef","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Elastic Net Regularization","order":4,"contentType":"LEARNING","contentSlug":"elastic-net","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_d81e4bbcf1af089a0e00779ec50c7594","title":"Combined L1/L2 Penalty - The Elastic Net Formulation","order":0},{"id":"seed_6d4c16f2b29898e4285d84eca2cbd8e5","title":"The Grouping Effect - Handling Correlated Features","order":1},{"id":"seed_aed479a1d15635b50b697103575fcc0d","title":"When to Use Elastic Net - Decision Framework","order":2},{"id":"seed_efc7146ca3a153d35cccd455da439d17","title":"Hyperparameter Selection - Tuning α and λ","order":3},{"id":"seed_e524ea838d1dc58a7958afe4b2c3f77c","title":"Practical Considerations - Implementation and Best Practices","order":4}]},{"id":"seed_01a0334a7189eee091e1369be1d858ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Bayesian Interpretation of Regularization","order":5,"contentType":"LEARNING","contentSlug":"bayesian-interpretation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_96a3322179113f456995f9d94bb886aa","title":"Prior Distributions","order":0},{"id":"seed_e7ba2c6b0d56c8a37695196c01101cd9","title":"Ridge as Gaussian Prior","order":1},{"id":"seed_33e61455247cfc0103a3b52c41eb9ea5","title":"Lasso as Laplace Prior","order":2},{"id":"seed_cf5153235af37459b83742855ad8afdf","title":"MAP Estimation","order":3},{"id":"seed_65f4100e8b885642ab9738682a137827","title":"Posterior Inference","order":4}]},{"id":"seed_539e0c0df2b9d3fc72bfe1ebe1830538","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 7: Regularization Theory","sectionOrder":10,"title":"Advanced Regularization Techniques","order":6,"contentType":"LEARNING","contentSlug":"advanced-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_0e1a8424672fa6d6854c7e4bbf0a35cf","title":"Group Lasso","order":0},{"id":"seed_32c6d5d2bd344c6dab4670ae261048b4","title":"Fused Lasso","order":1},{"id":"seed_18547794abe33afafaa35e72befe0479","title":"Bridge Regression","order":2},{"id":"seed_27b95f0db67e6780f5f9daeeca6d45a9","title":"Non-convex Penalties","order":3},{"id":"seed_5a7234f1fdeedf4868b4a74cad826ad5","title":"Structured Regularization","order":4}]},{"id":"seed_598afbdc7bc7fa0451b024fbbb08b0a1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Kernel Ridge Regression","order":1,"contentType":"LEARNING","contentSlug":"kernel-ridge-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_61d29cce4d0a2f0baf953d82931d2c0c","title":"Dual Formulation","order":0},{"id":"seed_942cdb30b511c6f602fa830ae9156036","title":"Kernel Matrix","order":1},{"id":"seed_d4d222769bf438256499bc494c8ee3de","title":"Prediction with Kernels","order":2},{"id":"seed_eaf5187dd53528848180a4f65853ee22","title":"Computational Complexity","order":3},{"id":"seed_94bd807fa06f2793f86be328bf1d845d","title":"Kernel Selection","order":4}]},{"id":"seed_723eff8f5cc227d1626cccbd013817f2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods","sectionOrder":11,"title":"Gaussian Processes Introduction","order":2,"contentType":"LEARNING","contentSlug":"gaussian-processes-introduction","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_1ff629386ecbd9ea503e727498e67afa","title":"GP as Distribution Over Functions","order":0},{"id":"seed_f065df7d5252178409e62f1671694ca4","title":"Prior Specification","order":1},{"id":"seed_30ebf4265054c973e021ae605f4c4bc4","title":"Posterior Inference","order":2},{"id":"seed_439bcae38aaaeff820f5d0eea97e8287","title":"Mean and Variance Prediction","order":3},{"id":"seed_7723bfbcf95297e50b31ebe3f5e84825","title":"GP vs Kernel Ridge","order":4}]},{"id":"seed_cab2603b0eff0b9065ccf19343b8f194","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"The Kernel Trick","order":1,"contentType":"LEARNING","contentSlug":"the-kernel-trick","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_1fa76caff1bb51ac49acd9308b6ab4e5","title":"Feature Space Mapping","order":0},{"id":"seed_2c41b9b3cc0701f340fa3941e197cc62","title":"Kernel Functions","order":1},{"id":"seed_80125b02ec588f7386dc54e2c9ad3be5","title":"Computational Advantage","order":2},{"id":"seed_cbc0699f5b8262ba515bc1430d3b7df6","title":"Mercer's Theorem","order":3},{"id":"seed_732643bdf91fe1019c973c38bea5c472","title":"Common Kernels","order":4}]},{"id":"seed_bc8bcb824c6863c43427f9eee6e7f874","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Gaussian Process Kernels and Hyperparameter Optimization","order":2,"contentType":"LEARNING","contentSlug":"gp-kernels-and-hyperparameters","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_b0606699677faeb2d1000e3442f0cb42","title":"Kernel Design: The Foundation of Gaussian Process Modeling","order":0},{"id":"seed_1c6091d51d8679c94308ad1738a86c3e","title":"Combining Kernels: Building Expressive Models","order":1},{"id":"seed_310979f943ba7d1e0080b946d68ef242","title":"Hyperparameter Optimization for Gaussian Processes","order":2},{"id":"seed_9e07db9cb43257ca92203b9665d9a174","title":"The Marginal Likelihood: Bayesian Model Selection","order":3},{"id":"seed_def1724cb40f5ad55866b232b04932fa","title":"Automatic Relevance Determination (ARD)","order":4}]},{"id":"seed_a8ccd12986493122718a2e8f610b8745","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Computational Aspects of Kernels","order":3,"contentType":"LEARNING","contentSlug":"computational-aspects-of-kernels","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7dbfa55ec53db8fc1c47c82e317e041e","title":"Kernel Matrix Complexity","order":0},{"id":"seed_51fe86cb860554294efeb648105e1b20","title":"Random Fourier Features","order":1},{"id":"seed_5add71fc85649f27507938d1a1ee8fdf","title":"Nyström Approximation","order":2},{"id":"seed_ed5ce57d6b6d7df6358974a55e4cd670","title":"Sparse Gaussian Processes","order":3},{"id":"seed_7ce4cec95f2d488acb433272c2232b8f","title":"Scalability Strategies","order":4}]},{"id":"seed_6242715024a6f8cd75cba825c4cf2e87","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel Selection and Design","order":4,"contentType":"LEARNING","contentSlug":"kernel-selection-and-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_9da5aed85bf1b36b85d7ff4287409925","title":"Kernel Families","order":0},{"id":"seed_dd691249e582ead6397a895f1932f384","title":"Domain-Specific Kernels","order":1},{"id":"seed_02cbd46742318300210d5a9785ac30fd","title":"Multiple Kernel Learning","order":2},{"id":"seed_967b6c680f5221491c7cf113462b2635","title":"Kernel Alignment","order":3},{"id":"seed_b496a55e6f91ac91782fd21262c67176","title":"Universal Kernels","order":4}]},{"id":"seed_766bbb1d8379c0966fce86e377d8aa45","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 8: Kernel Methods For Regression","sectionOrder":12,"title":"Kernel-Based Support Vector Classifier Training","order":5,"contentType":"PROBLEM","contentSlug":"kernel-support-vector-classifier","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.032Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[]},{"id":"seed_480cc6f8d716ebf111dd33d18fc8ce41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Polynomial Regression","order":1,"contentType":"LEARNING","contentSlug":"polynomial-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_5b12d1b3ac2c838155a1e47b728aa861","title":"Polynomial Features","order":0},{"id":"seed_40819aeac8f0eb99b896ef814b5e3924","title":"Degree Selection","order":1},{"id":"seed_2e45846fcd2d89e7c8309d4ac0a13946","title":"Overfitting Considerations","order":2},{"id":"seed_5982150413e065d9f3d8b7356ab888c9","title":"Orthogonal Polynomials","order":3},{"id":"seed_9bebfbc640e27943fcaa44c704ccb19f","title":"Piecewise Polynomials","order":4}]},{"id":"seed_6f3094d5cfb0e003129f88a934111408","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Spline Regression Methods","order":2,"contentType":"LEARNING","contentSlug":"spline-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_baf710dedf209853358c5a0b82fd849c","title":"Basis Splines (B-splines): The Foundation of Modern Spline Regression","order":0},{"id":"seed_c77bcf9dc30bf73ac93dad1753e6c11f","title":"Natural Splines: Optimal Boundary Behavior","order":1},{"id":"seed_9fb1e34712ba8dd5f1de915db3ca1d2c","title":"Smoothing Splines: The Optimization Framework","order":2},{"id":"seed_b26995ab647e7d9151911fb299645954","title":"Knot Selection: Strategies for Optimal Spline Flexibility","order":3},{"id":"seed_bd1a97174963a61038945c7aba819739","title":"Penalized Splines (P-splines): The Best of Both Worlds","order":4}]},{"id":"seed_fd56c51bbb955a03cd319577e559ca24","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Generalized Additive Models (GAMs)","order":3,"contentType":"LEARNING","contentSlug":"generalized-additive-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_91990fc3c684d915deda29466a200e0e","title":"Additive Structure","order":0},{"id":"seed_549dc5ac684ad125fe7a2a12bfddd9a9","title":"Component Functions","order":1},{"id":"seed_f1ae5bcded7464e5d28a5a5a2c1542e9","title":"Fitting Algorithms","order":2},{"id":"seed_32bcc722e4aa5bfef4732b81616b432e","title":"Interpretation","order":3},{"id":"seed_765b57d00b1ccdda5c262f086c407d7c","title":"Extensions","order":4}]},{"id":"seed_23ca6d0844f4fd9cf359b5e42366acae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Quantile Regression","order":4,"contentType":"LEARNING","contentSlug":"quantile-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_da2fc39be52dc34f8899f788c1b5bec5","title":"Quantile Loss Function","order":0},{"id":"seed_e3bc0a8f2bad307de48dc50902351f21","title":"Pinball Loss Function","order":1},{"id":"seed_4735e93544ee065c810528ee57495ec6","title":"Conditional Quantiles","order":2},{"id":"seed_9d6d1d25d2812571991ff8c60dbbe70e","title":"Prediction Intervals","order":3},{"id":"seed_d19a239cab2566296ca9b5234d2f22e6","title":"Robust Estimation","order":4}]},{"id":"seed_20d9052919ee9f6e5926ffc684095840","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Robust Regression Methods","order":5,"contentType":"LEARNING","contentSlug":"robust-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7379bb4970fae91e9a0dc5a987eb96f7","title":"Huber Loss: The Bridge Between L1 and L2","order":0},{"id":"seed_bccba192df86c8260ffb78eebb4da3da","title":"M-Estimators: The General Framework for Robust Estimation","order":1},{"id":"seed_1224b7833296543b9e6706e5c2497c98","title":"RANSAC: Random Sample Consensus for Extreme Outlier Robustness","order":2},{"id":"seed_a25af89e3140585e2503e7d85c172ed9","title":"Breakdown Point: The Ultimate Measure of Robustness","order":3},{"id":"seed_91862f58123af0f29bdb3ab65a4ade05","title":"Influence Functions: Understanding Local Robustness","order":4}]},{"id":"seed_d7ab2a7eb1f69de228f455ae070e7934","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 9: Advanced Regression","sectionOrder":13,"title":"Nonparametric Regression","order":6,"contentType":"LEARNING","contentSlug":"nonparametric-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_3cbe6308ac967d286d0449235b83352c","title":"Local Regression (LOESS/LOWESS)","order":0},{"id":"seed_054e9a035b1f855cbf2ab3c6eafc4e69","title":"Kernel Smoothing","order":1},{"id":"seed_a9342bbc00f47e961da1ab3700b4fc4c","title":"Bandwidth Selection","order":2},{"id":"seed_b9511280fb2a78bb01a308bb3600ab69","title":"Curse of Dimensionality","order":3},{"id":"seed_7089e4852501b5670aec7555bbd660d1","title":"When to Use Nonparametric Methods","order":4}]},{"id":"seed_78a01a27b8d25b02cf4b06660cd62e0f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression And Glms","sectionOrder":14,"title":"Regularized Logistic Regression","order":1,"contentType":"LEARNING","contentSlug":"module-5-regularized-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_88856996fd54d279febcbbe73fe3ff59","title":"L2 Regularization in Logistic Regression","order":0},{"id":"seed_f6d21c094bc3670091af8771243d7978","title":"L1 Regularization (Lasso) in Logistic Regression","order":1},{"id":"seed_ce94093763a7eba307abbe6f2e1f0a18","title":"Elastic Net Regularization in Logistic Regression","order":2},{"id":"seed_f583526ee776713b8383d5e6db04be0f","title":"Regularization Path","order":3},{"id":"seed_342fae968acb37817b71044d0e60ad3b","title":"Hyperparameter Selection","order":4}]},{"id":"seed_fd8976631490d38a493a49d4ea06d921","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Binary Classification Problem","order":1,"contentType":"LEARNING","contentSlug":"binary-classification-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_a4bcd986671cfa0e4fee85afc782ef2d","title":"Problem Formulation","order":0},{"id":"seed_f3f2afa2d1ac3b6aedca9e94a2b791bf","title":"Decision Boundaries","order":1},{"id":"seed_1657663f43e1973612cb12ee6ead2ff1","title":"Probabilistic Classification","order":2},{"id":"seed_888f539a93c61b5142fb32bb016e0e4e","title":"Linear Classifiers","order":3},{"id":"seed_c5bc31811892352ac9422f118f8e3ab9","title":"Loss Functions for Classification","order":4}]},{"id":"seed_a84bde3ef9a84b2740b90dbf47496bc4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"The Logistic Regression Model","order":2,"contentType":"LEARNING","contentSlug":"logistic-regression-model","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_7fc1febbf114c98ca5eae8e78d777c6d","title":"The Sigmoid Function","order":0},{"id":"seed_b87855bbcb2de262175a8f9861fffe43","title":"Log-Odds Interpretation","order":1},{"id":"seed_3a766c5c6697ac9b680f2bc5d0d3363b","title":"Model Parameters","order":2},{"id":"seed_4cb3ee219d08d0512d24de65d04f5777","title":"Decision Boundary","order":3},{"id":"seed_bb6c1b50bf1eaadb048b624616fa52ba","title":"Probabilistic Interpretation","order":4}]},{"id":"seed_7c3373282cd644a5e90d3ce19e000cae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Maximum Likelihood Estimation for Logistic Regression","order":3,"contentType":"LEARNING","contentSlug":"mle-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_369a33ed7306d84533926316bceffc1c","title":"Likelihood Function","order":0},{"id":"seed_17d45d86be758347f1482ddbbbd2de7a","title":"Log-Likelihood","order":1},{"id":"seed_3e6a7f027161604a73bce0837d4c73eb","title":"Gradient Derivation","order":2},{"id":"seed_5f1c74d1a1c88d7a3ad3a3281e13a67a","title":"No Closed-Form Solution","order":3},{"id":"seed_71e4de39fa4b22f420d160fc24d1bc71","title":"Optimization Approach","order":4}]},{"id":"seed_177b12207ca60d43846c02b30bdfc4ae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Multi-class Logistic Regression","order":4,"contentType":"LEARNING","contentSlug":"multi-class-logistic-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.136Z","pages":[{"id":"seed_cffe575643d8ccad5c8033a2fa050c1f","title":"The Softmax Function","order":0},{"id":"seed_6aa80a9b0be5561c4a4d14882fb2979e","title":"Multinomial Logistic Regression","order":1},{"id":"seed_a798362c2afa2313ccf38f5a4e029a77","title":"Cross-Entropy Loss","order":2},{"id":"seed_2e62e548b8a8c7c600a30c411da4ba2a","title":"One-vs-All Strategy","order":3},{"id":"seed_68ff5c7da93b4c9040e7096f8cf39441","title":"Computational Considerations","order":4}]},{"id":"seed_700a56229a39f2c5efb792c516b003de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Generalized Linear Models (GLMs)","order":5,"contentType":"LEARNING","contentSlug":"generalized-linear-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_61f49d503c29f3aaa9fe97060f0e84ce","title":"The GLM Framework: A Unified Theory of Regression","order":0},{"id":"seed_e5abdd3b0fefa03a337ebde0ba5d9309","title":"Link Functions: Connecting Means to Predictors","order":1},{"id":"seed_d80306c03aad79af263044607dad82e2","title":"The Exponential Family: Foundation of GLMs","order":2},{"id":"seed_4d96766c2c3d8f0b2376f3986d899f79","title":"Poisson Regression: Modeling Count Data","order":3},{"id":"seed_8f0eec87fd2cb9fac66f6b58fc301d19","title":"Negative Binomial Regression: Handling Overdispersion","order":4}]},{"id":"seed_5b5ecd2ea6321d0e366b6369851d7e1f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":6,"contentType":"VISUALIZER","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_cc24bec346a9c06530806b63a487f8db","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Logistic Sigmoid Transformation","order":7,"contentType":"PROBLEM","contentSlug":"sigmoid-activation-function","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_94cb522600865b629be81feefa09685d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 10: Logistic Regression Glms","sectionOrder":15,"title":"Single Neuron Binary Classifier with Sigmoid Activation","order":8,"contentType":"PROBLEM","contentSlug":"single-neuron-binary-classifier","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_dceafab2ca6be5ae2808a6312c2333dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Decision Tree Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"decision-tree-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_db6bac32f9c8fa3cd80510e8bd10d766","title":"Tree Structure","order":0},{"id":"seed_5c52be5dceb94348b3ce82b515f1f7b6","title":"Splitting Rules","order":1},{"id":"seed_a51f17b5d5a4d499688b830e43ca4504","title":"Leaf Predictions","order":2},{"id":"seed_7452c05ddbfdef9b478bcf2b5561d0be","title":"Recursive Partitioning","order":3},{"id":"seed_497e181ba832cf2f400fa4dd970b0051","title":"Interpretability","order":4}]},{"id":"seed_66f262a333c03eff5f0ef18781c9d3af","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Splitting Criteria","order":2,"contentType":"LEARNING","contentSlug":"splitting-criteria","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2f7e3d1f8f264924373b072cf1d7e2a6","title":"Gini Impurity: The Measure of Node Purity","order":0},{"id":"seed_1c9d698a70dc23f6de89558e90031fb7","title":"Entropy and Information Gain: The Information-Theoretic Foundation","order":1},{"id":"seed_27a597247baed7e09a847ba1c07643c3","title":"Misclassification Error: Simplicity at a Cost","order":2},{"id":"seed_8e31a4e9800e6d320ab6e525e75f99dc","title":"Comparison of Splitting Criteria: A Unified Perspective","order":3},{"id":"seed_8d0db373d93d1c18f67cd2710cedd60a","title":"Continuous Feature Splitting: Finding Optimal Thresholds","order":4}]},{"id":"seed_75b193ee8207ca9f7b2d2866489de306","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Growing Algorithms","order":3,"contentType":"LEARNING","contentSlug":"tree-growing-algorithms","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f0288d0b463a538ef9839e2b152e1fe8","title":"ID3 Algorithm","order":0},{"id":"seed_45a00dbb8f00e9e8fa5239c18fc72b75","title":"C4.5 Algorithm","order":1},{"id":"seed_acecb5290d37b28a1658150b952137c5","title":"CART Algorithm","order":2},{"id":"seed_19c3b7533c2361525fbcec0a7dc2a7d6","title":"Handling Missing Values","order":3},{"id":"seed_fa7eec5cef5065c7f8388ccd2906507b","title":"Categorical Features","order":4}]},{"id":"seed_2460c7b303e40cec69e89a72b8b831e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Pruning and Regularization","order":4,"contentType":"LEARNING","contentSlug":"pruning-and-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_841da38741645dea8f1d16766d4097c2","title":"Pre-pruning: Early Stopping Strategies","order":0},{"id":"seed_0684bbba0f126aa161a3b10443da2454","title":"Post-pruning: Cost-Complexity Pruning","order":1},{"id":"seed_7c8fec22d1e66772a261bf4b028b671d","title":"Minimum Samples: Statistical Constraints on Tree Growth","order":2},{"id":"seed_f8971d9e2c41aa674e3af7282f56f977","title":"Maximum Depth: Controlling Tree Complexity","order":3},{"id":"seed_e251446ee8db54d758a4f7a260ec06c6","title":"Minimum Impurity Decrease: Split Quality Threshold","order":4}]},{"id":"seed_f4d666cbe20d00df3882c0b2203eb2c8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Regression Trees","order":5,"contentType":"LEARNING","contentSlug":"regression-trees","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ac677a4cffa4eb4b8e7494abddfe864","title":"CART for Regression","order":0},{"id":"seed_5c865b327c14cb2ba7d85d96e8a7d6ff","title":"Splitting Criterion (MSE)","order":1},{"id":"seed_c7dd375c88d35491e1c784ea8844cfcf","title":"Leaf Predictions","order":2},{"id":"seed_b042f5d3ad71aedacdc24b113c501dbe","title":"Piecewise Constant Approximation","order":3},{"id":"seed_9443337f0d6c814c51135b3ae8d7e3be","title":"Comparison to Linear Models","order":4}]},{"id":"seed_60576ab2af9ebaac4b42cd4cfc97570c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Tree Limitations and Extensions","order":6,"contentType":"LEARNING","contentSlug":"tree-limitations-and-extensions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c74c7553d6afe87d76c584a3baa95199","title":"Instability — The Achilles' Heel of Decision Trees","order":0},{"id":"seed_f2ffb97ca33ce7f4768bac5dcd430770","title":"Axis-Aligned Splits — The Geometric Constraint of Standard Trees","order":1},{"id":"seed_d4c206fc75437a3e8bf85b62dd8ab811","title":"Oblique Trees — Breaking the Axis-Aligned Constraint","order":2},{"id":"seed_a54f1c9c1e5fd1e0a0eaf827bab9c229","title":"Multivariate Splits — Beyond Linear Combinations","order":3},{"id":"seed_1e367502710bf09157f993258c864920","title":"Connections to Other Methods — Trees in the ML Landscape","order":4}]},{"id":"seed_5ecf05e48f7a0b21063fc124157e3b41","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 11: Decision Trees","sectionOrder":16,"title":"Recursive Classification Tree Builder","order":7,"contentType":"PROBLEM","contentSlug":"recursive-classification-tree","description":null,"estimatedMins":45,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6694bec14cdf4dec18589122c2ef6ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Maximum Margin Classifier","order":1,"contentType":"LEARNING","contentSlug":"maximum-margin-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a013e659a0da4e748d8f4fd2bc521820","title":"Geometric Margin: The Distance to Decision Boundaries","order":0},{"id":"seed_8e564baf204caedfc53ee24d6da469ce","title":"Functional Margin: The Raw Classification Score","order":1},{"id":"seed_7781928edfa2f6a8db48d6d63d0c6813","title":"Maximum Margin Objective: The Optimization Principle","order":2},{"id":"seed_99ee1607d1d433d7d55647989482db1c","title":"Support Vectors: The Critical Points","order":3},{"id":"seed_a0949455a613e6979bf3ffcd5ef97bdc","title":"Uniqueness of Solution: One Optimal Hyperplane","order":4}]},{"id":"seed_718289c9fa43b9c118e2c33a9b999fc7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Hard Margin SVM","order":2,"contentType":"LEARNING","contentSlug":"hard-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bd24e84928c4e49d5fb02c02261d93bf","title":"Optimization Problem Formulation","order":0},{"id":"seed_80314c8a4df8cf9418973501c6118eaa","title":"Quadratic Programming","order":1},{"id":"seed_441288891ef7e962e37273a2e7253aa6","title":"Lagrangian Derivation","order":2},{"id":"seed_19c4ff4e9c0b7b17c4ae1703468a3574","title":"Dual Problem","order":3},{"id":"seed_49a4b6463f97e10918e9e3d3fb8ee23a","title":"KKT Conditions","order":4}]},{"id":"seed_a42c0383f3c3f0b2cfe805c1906ef67e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Soft Margin SVM","order":3,"contentType":"LEARNING","contentSlug":"soft-margin-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.269Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bfbd75f04834342b65318edbb84113c2","title":"Slack Variables: Tolerating Margin Violations","order":0},{"id":"seed_86a65a3533abd701056463e3dd5af455","title":"Hinge Loss: The Margin-Aware Loss Function","order":1},{"id":"seed_3354a76472b33953793b7c6fbd5b964a","title":"The C Parameter: Controlling the Margin-Error Trade-off","order":2},{"id":"seed_4b0add25426ebfe1fbb82d0bfa2f8221","title":"Dual Formulation: The Gateway to Kernel Methods","order":3},{"id":"seed_38e22c3634cc6b6e3b45c9f5be79a956","title":"Soft Margin Interpretation: The Complete Picture","order":4}]},{"id":"seed_0817db7f4e635c79befbd2ec867d4208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Kernel Support Vector Machines","order":4,"contentType":"LEARNING","contentSlug":"kernel-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dce0554e23e92f7f29832689e1e9033a","title":"Dual Form Advantage","order":0},{"id":"seed_b43d07d4cb4e2b4f7f41ecbf8e25aa74","title":"Kernel Substitution","order":1},{"id":"seed_0904460c8033632b21a801e3a4e9a9d6","title":"Common Kernels","order":2},{"id":"seed_f8e58f4809afca84d915ca2f03254da7","title":"Nonlinear Boundaries","order":3},{"id":"seed_305c4e03a791712a752734f5684cc029","title":"Kernel Selection","order":4}]},{"id":"seed_7f7f1f41aaf42cfff875745539c688c2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"SVM Optimization","order":5,"contentType":"LEARNING","contentSlug":"svm-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_541b80cdd5f2cd1733632be6b953271a","title":"Sequential Minimal Optimization (SMO) Algorithm","order":0},{"id":"seed_09139165234e636a41838eeeaddd00e4","title":"Working Set Selection","order":1},{"id":"seed_a8401f3bebe2a52cdf978b1c13f1e9a6","title":"Convergence","order":2},{"id":"seed_8754eab4b8b19725779915bae5340575","title":"Complexity Analysis","order":3},{"id":"seed_72ea95f54fd26d4cea95cd853ca0ed85","title":"Practical Implementations","order":4}]},{"id":"seed_9d6a6fae9d4733d75d9d2c0ef2bf6689","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 12: Support Vector Machines","sectionOrder":17,"title":"Multi-class Support Vector Machines","order":6,"contentType":"LEARNING","contentSlug":"multi-class-svm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4b1f69f046d5328e99ff707789aa02a9","title":"One-vs-One (OvO) Strategy","order":0},{"id":"seed_1b7fa8150c8864265c58f0e10b9ea16b","title":"One-vs-All (OvA) Strategy","order":1},{"id":"seed_e64777f669b5bf01032b4810e325a971","title":"DAG-SVM (Directed Acyclic Graph SVM)","order":2},{"id":"seed_5223356c350f2faabbc988c6682d4ab7","title":"Crammer-Singer Formulation","order":3},{"id":"seed_423e270eb005d730e9fabd5874a5bf9c","title":"Practical Considerations for Multi-class SVM","order":4}]},{"id":"seed_2db3a376585980bc271d7b13445f187d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Gaussian Naive Bayes","order":1,"contentType":"LEARNING","contentSlug":"gaussian-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e97c002711fccb4eeeed7265c885efd3","title":"Continuous Features","order":0},{"id":"seed_ab154327f4572e2e7487faa04b589a0d","title":"Gaussian Assumption","order":1},{"id":"seed_e7e8aedb1b2e0e515d90f215b1354689","title":"Parameter Estimation","order":2},{"id":"seed_88c3971753f5766f9ca133581fc35266","title":"Decision Boundary","order":3},{"id":"seed_652624afe940275cedd923d401825e0b","title":"Connection to LDA","order":4}]},{"id":"seed_ccb1182e88952a8e41eacf6988a8f962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes And Probabilistic Classifiers","sectionOrder":18,"title":"Linear and Quadratic Discriminant Analysis","order":2,"contentType":"LEARNING","contentSlug":"linear-and-quadratic-discriminant-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b663c68ea45a6ca1fbbb583ac9112c9b","title":"LDA Assumptions","order":0},{"id":"seed_2a1672c563bf8cb2570451b534334aaa","title":"Shared Covariance","order":1},{"id":"seed_30f3a8e32e9273fea8218dea1e695c0e","title":"QDA with Class-Specific Covariance","order":2},{"id":"seed_726eaff8e9d2a81756f201183fb245e9","title":"Decision Boundaries","order":3},{"id":"seed_ac2af5894584a1c6d2d5591f34b9eaea","title":"Regularized Discriminant Analysis","order":4}]},{"id":"seed_fbe1f6cf1357ee3876023e84eeebe2e1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Generative vs Discriminative Models","order":1,"contentType":"LEARNING","contentSlug":"generative-vs-discriminative","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c8b007934c88097a3916616249da339e","title":"Generative Model Formulation","order":0},{"id":"seed_d786ec7f230276f2cdd80df406a8046b","title":"Discriminative Model Formulation","order":1},{"id":"seed_e3ab381760a46322c1bacf38bef47bbc","title":"Pros and Cons: A Comprehensive Comparison","order":2},{"id":"seed_1625278a8125ba9bbac6b390ac550c83","title":"When to Use Each: Practical Decision Framework","order":3},{"id":"seed_dcc3442b4449518a1ae900f3f27db1f3","title":"The Famous Debate: Ng & Jordan (2001)","order":4}]},{"id":"seed_1cc60ba5baf88bab74e808a1eb6e245f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Bayes Classifier","order":2,"contentType":"LEARNING","contentSlug":"bayes-classifier","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3d10da113ce802c8a852c5b96c4aad7a","title":"Bayes Decision Rule","order":0},{"id":"seed_36207040873f9070196a6f07f1070292","title":"The Optimal Classifier","order":1},{"id":"seed_e359cdddffb130d0cf1a2816e38a0a4c","title":"Bayes Error Rate","order":2},{"id":"seed_732f83c1286963163d871881937c9e2f","title":"Posterior Computation","order":3},{"id":"seed_6d9701ee09ac8c68d18443eeaf6b9e2f","title":"Practical Challenges","order":4}]},{"id":"seed_b88cd36a22d2096598e1f8434cad7cbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"The Naive Bayes Assumption","order":3,"contentType":"LEARNING","contentSlug":"naive-bayes-assumption","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_71a76992bb037b07b38fff7e6be4d220","title":"Conditional Independence","order":0},{"id":"seed_e10c8c25e6a31d2df94595f86a47e1f8","title":"When the Assumption Holds","order":1},{"id":"seed_f5aa87e2cf27bc16e0787c8b3e414288","title":"When the Assumption Fails","order":2},{"id":"seed_301dba405088214629dda64de083094e","title":"Still Works Surprisingly Well","order":3},{"id":"seed_bb9a2adbeda94ac7dc465728342e67ea","title":"Theoretical Analysis","order":4}]},{"id":"seed_c827e845d309186b4d828114f01bf6ea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 13: Naive Bayes Probabilistic Classifiers","sectionOrder":19,"title":"Multinomial and Bernoulli Naive Bayes","order":4,"contentType":"LEARNING","contentSlug":"multinomial-bernoulli-naive-bayes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7039ddc6e6dec09f66328dae7cd724e1","title":"Text Classification","order":0},{"id":"seed_66282f75590576422d0c8b6bbf93d88e","title":"Multinomial Naive Bayes Model","order":1},{"id":"seed_58ae07ae636beb76783fa9de70eed98d","title":"Bernoulli Naive Bayes Model","order":2},{"id":"seed_d6ab95eb1edb7c82c2baae5c49129b10","title":"Laplace Smoothing","order":3},{"id":"seed_f5eae1287c6ed688b015bc60cc1ad757","title":"Comparing Multinomial and Bernoulli Naive Bayes","order":4}]},{"id":"seed_8e0dbdfdcaf7222bdf2b6330b80049eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Algorithm","order":1,"contentType":"LEARNING","contentSlug":"knn-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b3ef7c78abad802522bced6f8b593088","title":"Instance-based Learning","order":0},{"id":"seed_92702947917f121e7491381b08b81653","title":"Lazy Learning","order":1},{"id":"seed_b3a1d09a31edd56725996d96a4b3ec49","title":"K Selection","order":2},{"id":"seed_458eeb6b81ba4009afd003599aa716b2","title":"Voting Schemes","order":3},{"id":"seed_de99956f6dd8c49e6334359c9e1dc045","title":"Algorithm Pseudocode","order":4}]},{"id":"seed_9a886703f21dde8f994accd7696e3962","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Distance Metrics for K-Nearest Neighbors","order":2,"contentType":"LEARNING","contentSlug":"distance-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cc37f06bb61f5ab2554947a5d8f04678","title":"Euclidean Distance","order":0},{"id":"seed_3d7c996c0693354f6ee63a0f9951377f","title":"Manhattan Distance","order":1},{"id":"seed_c10355bdf63bb5a21e338f040119a32c","title":"Minkowski Distance","order":2},{"id":"seed_0215ac1dc6550fbb45632681d85c8c7f","title":"Cosine Similarity","order":3},{"id":"seed_5c498fed2336c119293ffe28ccb9f584","title":"Custom Distances","order":4}]},{"id":"seed_beabe40abd8e31e1a3461563e488e5ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Curse of Dimensionality","order":3,"contentType":"LEARNING","contentSlug":"curse-of-dimensionality","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a25701afefc8d1c19f74621dd381cac2","title":"Distance Concentration","order":0},{"id":"seed_9793aa996f0eb675f6de731a34b2c580","title":"Sparsity in High Dimensions","order":1},{"id":"seed_5fb07d6347eef101bae630aeb875fc22","title":"Volume of Hyperspheres","order":2},{"id":"seed_f3f2bbd9c93fe19dd118e4f9a2846696","title":"Implications for KNN","order":3},{"id":"seed_dcdd97dc500b5459250187469a9bdc5f","title":"Mitigation Strategies","order":4}]},{"id":"seed_a3e281d02cea4be01efa2e8de9d21650","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Efficient K-Nearest Neighbors","order":4,"contentType":"LEARNING","contentSlug":"efficient-knn","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_767b8623bade70774755f07ea7b14276","title":"Brute Force Complexity","order":0},{"id":"seed_f3992bc86691be1608de8b1c11595164","title":"KD-Trees (Construction and Search)","order":1},{"id":"seed_9439553638b58a064b81b1a0a669df26","title":"Ball Trees","order":2},{"id":"seed_8820b1217773d0e969c6fbff1d27fd03","title":"Voronoi Diagrams","order":3},{"id":"seed_b7a48c9ece938fe502ba756a6f8da019","title":"Locality-Sensitive Hashing (LSH)","order":4},{"id":"seed_b0c19ec065e24656574a793db5afeeb3","title":"Approximate Nearest Neighbors","order":5}]},{"id":"seed_48ce033aa3506c796b6262f9edcf55a6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Voronoi Diagrams & Decision Surfaces","order":5,"contentType":"LEARNING","contentSlug":"voronoi-diagrams-and-decision-surfaces","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ed15082a903245caac2646e9f1b0707d","title":"Voronoi Tessellation","order":0},{"id":"seed_9f686904baeb073c51ffd116b1e55173","title":"Decision Boundaries as K Changes","order":1},{"id":"seed_48e91cb238d93f16b350b58847790ade","title":"1-NN Creates Voronoi Cells","order":2},{"id":"seed_d93d8af8669b7b1bf2d0b1515c1db018","title":"Visualization Techniques","order":3},{"id":"seed_899ce2078ad4c1fbfcb5437be630e0f5","title":"Decision Surface Smoothing with K","order":4}]},{"id":"seed_4186620863285c1d7b3ce960bbd55ecf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Locality-Sensitive Hashing (LSH)","order":6,"contentType":"LEARNING","contentSlug":"locality-sensitive-hashing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_422fc067c242a6c1592bfdc49657350d","title":"Hashing vs Locality-Sensitive Hashing","order":0},{"id":"seed_987cdd3db17aeb065a3b73d1748b3ab2","title":"LSH for Cosine Similarity","order":1},{"id":"seed_11e00ed6e7f9795dae476b69160f939d","title":"LSH for Euclidean Distance","order":2},{"id":"seed_b0274b47970102b6f6a1bc16dbb7765a","title":"Hash Function Families","order":3},{"id":"seed_b00b68cd39352ab9b7cc9a215ab64bf3","title":"Collision Probability Analysis","order":4},{"id":"seed_4ae869636b1a7b8510688cf03f021a04","title":"LSH Performance Tuning","order":5}]},{"id":"seed_02e815b47429e576631e0381220c4966","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"Weighted KNN","order":7,"contentType":"LEARNING","contentSlug":"weighted-knn","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c0d21453491f6814f6c1e82b7cdb03fa","title":"Distance-Weighted Voting","order":0},{"id":"seed_0c77828567376cbec542352769b007c3","title":"Kernel-Weighted KNN","order":1},{"id":"seed_367df2ca69735b580bcf7d45c917f771","title":"Adaptive Neighborhoods","order":2},{"id":"seed_7a1bb2b4d7b064b03b362fc25377e69e","title":"Local Models","order":3},{"id":"seed_979f6a7e5374163fd77e264b1f8e30fd","title":"Regression with KNN","order":4}]},{"id":"seed_63ff05a505bafec47763bc44bff3a983","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 14: K Nearest Neighbors","sectionOrder":20,"title":"KNN Variants","order":8,"contentType":"LEARNING","contentSlug":"knn-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_852693e826b7b366fc698220dd5ab126","title":"Condensed Nearest Neighbors","order":0},{"id":"seed_396e2e4b3c6afabaf32e843498ccd1e4","title":"Edited Nearest Neighbors","order":1},{"id":"seed_ed592d7b5e978e26879209ca7e2c959e","title":"Large Margin K-Nearest Neighbors","order":2},{"id":"seed_15ae9066457b0afbe32ad7e477f75ffe","title":"Metric Learning","order":3},{"id":"seed_880c94be4a738368403f33ec2a2af9df","title":"KNN Ensemble Methods","order":4}]},{"id":"seed_bafb1e8a6af32317a42e273f5a15de1c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Ensemble Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"ensemble-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d5ae93f6a322acdc0c106281d92ad6d9","title":"Why Ensembles Work","order":0},{"id":"seed_bb68e302e90e329001dbbf2117b0c1d0","title":"Wisdom of Crowds","order":1},{"id":"seed_c7e0c238cbbeaccc7f7ef573b1357249","title":"Error Decomposition","order":2},{"id":"seed_d7d99c150d01c767dd84ce792359a169","title":"Diversity Requirement","order":3},{"id":"seed_237fc0e1ef894fc4db67f7b70178939a","title":"Ensemble Strategies","order":4}]},{"id":"seed_22c2fdccde0ab9e52f64339f364e757d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Random Forests","order":2,"contentType":"LEARNING","contentSlug":"random-forests","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f206c4e9769ba52a5a5337678ddba40a","title":"Random Subspace Method","order":0},{"id":"seed_7c8554e88c301160e491ad7f2045acfd","title":"Feature Randomization","order":1},{"id":"seed_36571f6077f8546d0f635286e5d023e0","title":"Tree Correlation Reduction","order":2},{"id":"seed_0111e6c4513c2938886ae44dd6609f70","title":"Hyperparameters","order":3},{"id":"seed_5dbd3c3660e45b6890f624d91c2f8774","title":"Parallelization","order":4}]},{"id":"seed_00a4486bae311423e333adfc96315696","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging And Random Forests","sectionOrder":21,"title":"Bagging for Other Models","order":3,"contentType":"LEARNING","contentSlug":"bagging-for-other-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f17b473b206d45a4963ce1982e311cd8","title":"Bagged Decision Trees","order":0},{"id":"seed_d2546d82a8e25fd32de0addc8eac9c22","title":"Bagged Neural Networks","order":1},{"id":"seed_2800b27da94dd7e25ee1ef83acaecf69","title":"Model Aggregation Strategies","order":2},{"id":"seed_f74c790d98bfc3f02c4a1b2dc1767bae","title":"Bagging vs Boosting","order":3},{"id":"seed_e271e771407a1ebbcf2227430171665e","title":"When Bagging Helps","order":4}]},{"id":"seed_c51429550b08bde8708ef8c1c162ef1d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Bootstrap Aggregating: Variance Reduction Through Ensemble Averaging","order":1,"contentType":"LEARNING","contentSlug":"bootstrap-aggregating","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6c0316bc295dad900b4c9708c20a72fc","title":"Bootstrap Sampling: The Statistical Foundation of Bagging","order":0},{"id":"seed_d0435232560eee440ff892d090415ddb","title":"Aggregation Methods: Combining Predictions for Ensemble Power","order":1},{"id":"seed_6f1f9995617ac89e722490361c1d689d","title":"Variance Reduction: The Mathematical Heart of Bagging","order":2},{"id":"seed_bca67afd5ce45056b9c8e71e3adba2da","title":"Bias Preservation: Why Bagging Doesn't Increase Systematic Error","order":3},{"id":"seed_713633bd98f3959d3237ce13d9456cf2","title":"Out-of-Bag Estimation: Free Validation in Bagged Ensembles","order":4}]},{"id":"seed_51f41e3368033fc0b10899ff25d5c5ac","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Feature Importance","order":2,"contentType":"LEARNING","contentSlug":"feature-importance","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ce10f972faaec2f14d6203a335fd01ab","title":"Impurity-based Importance","order":0},{"id":"seed_02f98b069cefc31e4c0b227570f23b0b","title":"Permutation Importance","order":1},{"id":"seed_a3d2cc9f29a4b681486ab2e469a5c07f","title":"Drop-Column Importance","order":2},{"id":"seed_b55d0c9d2b705a96995dc4079b9b43a2","title":"Bias in Importance","order":3},{"id":"seed_274a631bff1b01c8032fca70fb006813","title":"Interpretation Guidelines","order":4}]},{"id":"seed_5ad2b982504501af216bbc78e377ae2a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 15: Bagging Random Forests","sectionOrder":22,"title":"Random Forest Variants","order":3,"contentType":"LEARNING","contentSlug":"random-forest-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e78bf7e2fdf7e3ebd10936630916d089","title":"Extremely Randomized Trees (Extra-Trees)","order":0},{"id":"seed_8f6d7cf5fab415ae55ddc7f281342bf0","title":"Rotation Forests","order":1},{"id":"seed_51ec2c44de67b1623dc9979d615c6c3c","title":"Random Patches","order":2},{"id":"seed_c8d2077b09ada53e477b319fee18d7a8","title":"Subspace Forests","order":3},{"id":"seed_3f5f370912bce10fec92dd4f33906f54","title":"Oblique Random Forests","order":4}]},{"id":"seed_f03dc46090058acd74b559494ee32f72","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting Intuition","order":1,"contentType":"LEARNING","contentSlug":"boosting-intuition","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_98ca86a4e9390242ea140586859fb175","title":"Weak Learners","order":0},{"id":"seed_eef1b969f20d08017b67936fe0520739","title":"Sequential Combination","order":1},{"id":"seed_7021c8a2344039ec4656418c52002283","title":"Focus on Errors","order":2},{"id":"seed_e00ae9c3b7d46fa2b68612e9048a83ad","title":"Strength Amplification","order":3},{"id":"seed_9c8238d5d70f52460347f19ed7f9f86e","title":"Historical Development","order":4}]},{"id":"seed_013f75c45c26c5238649f80602c7eea3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"AdaBoost: Adaptive Boosting","order":2,"contentType":"LEARNING","contentSlug":"adaboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9f9509db19eb4a045548fb42acc478a4","title":"Adaptive Boosting Algorithm","order":0},{"id":"seed_b0ef2a1154c2561572a94854fcee8d64","title":"Weight Updates","order":1},{"id":"seed_c7c9c97ada179ecb486c200accc4fc6f","title":"Weighted Voting","order":2},{"id":"seed_348190b4ed209df5ea6f490204fe09a9","title":"Training Error Bounds","order":3},{"id":"seed_24e976d10f8f97156a4382c0db3dca8f","title":"Exponential Loss Connection","order":4}]},{"id":"seed_747bf0ebc82590cbd3552b19fb0b76e9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Boosting as Gradient Descent","order":3,"contentType":"LEARNING","contentSlug":"boosting-as-gradient-descent","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ce231cc963a00c8d9cdec279d1c21fa6","title":"Functional Gradient Descent","order":0},{"id":"seed_631a9be40b4f0e974be95aea0c162dca","title":"Additive Models","order":1},{"id":"seed_c913caed106b3fd4ca17d8670d868a34","title":"Stage-wise Optimization","order":2},{"id":"seed_322dc9aa1b121b02998bb0bab670965a","title":"Loss Function Formulation","order":3},{"id":"seed_c66dcc7ae3692a748ffb079bd37409f8","title":"Forward Stagewise Additive Modeling","order":4}]},{"id":"seed_51f106abb54ddee63c6fb37b54ff5b4a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Gradient Boosting","order":4,"contentType":"LEARNING","contentSlug":"gradient-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_79ce96b4b51b651908940c2ba9fb85c1","title":"Gradient Boosting Algorithm","order":0},{"id":"seed_df76c44846278e19cd9b1c54ff1885a9","title":"Pseudo-Residuals","order":1},{"id":"seed_2e7b77b7bce0d05f2fcde734ea026212","title":"Base Learner Fitting","order":2},{"id":"seed_83366531d92b0d16baccc7e3488d7bfc","title":"Step Size (Learning Rate)","order":3},{"id":"seed_ba13e031492bda2444d32a7f65e054d9","title":"Stopping Criteria","order":4}]},{"id":"seed_dc5adc297f1955b0a18ca701a518df00","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Loss Functions for Boosting","order":5,"contentType":"LEARNING","contentSlug":"loss-functions-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c3eeae4b8adb94712ed56dc03190aff8","title":"Squared Loss (Regression)","order":0},{"id":"seed_fed01c3688f0c351a239bdde913abbca","title":"Absolute Loss","order":1},{"id":"seed_e5bacf98767f3866627c074cf0efacdf","title":"Huber Loss","order":2},{"id":"seed_96cac975c288f2256792a91f178807d2","title":"Logistic Loss (Classification)","order":3},{"id":"seed_556e7382a179e706571235d8bb2d391b","title":"Custom Loss Functions","order":4}]},{"id":"seed_8af9b78e83ce53d210e580fa87ed569b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 16: Boosting Theory","sectionOrder":23,"title":"Regularization in Boosting","order":6,"contentType":"LEARNING","contentSlug":"regularization-in-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a836d91b120a3264e891b66c91ecda00","title":"Shrinkage (Learning Rate)","order":0},{"id":"seed_bab2ca4cb3d208d4ecbc44a456c0754d","title":"Subsampling (Stochastic Gradient Boosting)","order":1},{"id":"seed_aa794853cc8545c3fe9ddcfd322e7077","title":"Tree Constraints","order":2},{"id":"seed_2e6574ef7caadb7315d805c32fc2271e","title":"Early Stopping","order":3},{"id":"seed_eaee9b00d47ee8d6aa36b6933e09d5c0","title":"L1/L2 Regularization","order":4}]},{"id":"seed_fb17f19632cfed0944218bf0287a019f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"XGBoost: Extreme Gradient Boosting","order":1,"contentType":"LEARNING","contentSlug":"xgboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_70199ea37552e815d30cd1e4e1e1a96e","title":"Regularized Objective","order":0},{"id":"seed_fc960362e857ca3253deb02648981ebc","title":"Second-Order Approximation","order":1},{"id":"seed_3aa4db9638718d1f047e9ce3c5958c99","title":"Efficient Split Finding","order":2},{"id":"seed_ec0b115e474f81dce25b417d03408843","title":"Sparsity-Aware Algorithm","order":3},{"id":"seed_a6ddcd6fe2a2752c06d1caa528c409f9","title":"System Optimizations","order":4}]},{"id":"seed_f4ed4ac2f15096908a3839a371aea45d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"LightGBM: Light Gradient Boosting Machine","order":2,"contentType":"LEARNING","contentSlug":"lightgbm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b372e5bb5fe0d674a5dcc204db08433b","title":"Leaf-wise Tree Growth","order":0},{"id":"seed_12dce22898a5635acc15a2926974f893","title":"Gradient-based One-Side Sampling (GOSS)","order":1},{"id":"seed_b0c60ddce5a674de0633f91754a7ca0f","title":"Exclusive Feature Bundling (EFB)","order":2},{"id":"seed_4838bfc1b22480cc4ffb5bfb5ee5f893","title":"Histogram-based Splitting","order":3},{"id":"seed_2a537c9c9e3a285c0545625ab68f3224","title":"Speed Comparison","order":4}]},{"id":"seed_2c9e0dfc3b67f3a1c9330f30e269636b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"CatBoost","order":3,"contentType":"LEARNING","contentSlug":"catboost","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3930f8b1ed807d4ece32050803e0f427","title":"Ordered Boosting","order":0},{"id":"seed_c83ee840f4e102e8edbc47fe0327c551","title":"Categorical Feature Handling","order":1},{"id":"seed_0f63c88bbb221b89e0692e9225b69c0b","title":"Target Statistics","order":2},{"id":"seed_899aaf6e82fd390d644516dc0f937818","title":"Symmetric Trees","order":3},{"id":"seed_6c6b606e013767ff1459da935a9b6731","title":"GPU Acceleration","order":4}]},{"id":"seed_8a969e8156debd6e794f66d4ba1b8f47","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Hyperparameter Tuning for Boosting","order":4,"contentType":"LEARNING","contentSlug":"hyperparameter-tuning-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ab93c270234af71b045e0331980ae611","title":"Key Hyperparameters","order":0},{"id":"seed_d900530b9fe873a5a86ec75b05b1c723","title":"Learning Rate and Iterations","order":1},{"id":"seed_e144a6ff394ec3a69eecbf7297ec4f93","title":"Tree-Specific Parameters","order":2},{"id":"seed_c78b2620e23f30ec5c56f818a8bff473","title":"Regularization Parameters","order":3},{"id":"seed_c6e2bc3a0f056809e3191424e6a4bf34","title":"Tuning Strategies","order":4}]},{"id":"seed_b3cc7ecb9af183e57d91f1d32acd0ef2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Feature Engineering for Boosting","order":5,"contentType":"LEARNING","contentSlug":"feature-engineering-for-boosting","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1ad793ee0751b514ebf7d490b1b952fc","title":"Feature Interactions","order":0},{"id":"seed_bb14d8bf8c61fc7a57de9ef09da35e70","title":"Target Encoding","order":1},{"id":"seed_5f41f5b1d125bd6f335516a6bfb2f71f","title":"Frequency Encoding","order":2},{"id":"seed_a07c750ff7cb96a3ff84ad2a324cef28","title":"Categorical Handling","order":3},{"id":"seed_6fa2913074e9d9a1163d3b94ebfab7aa","title":"Feature Selection","order":4}]},{"id":"seed_0f0a3fcb7bd62041bb7bef139cbd5bdc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 17: Modern Boosting","sectionOrder":24,"title":"Boosting Best Practices","order":6,"contentType":"LEARNING","contentSlug":"boosting-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0cfac23065070fd5a2a40b9d073376a7","title":"Training Monitoring","order":0},{"id":"seed_9319321188b2e2866f475f7a2d580793","title":"Early Stopping","order":1},{"id":"seed_cc82cb532dc200977d249bf5f116ae94","title":"Cross-Validation for Boosting","order":2},{"id":"seed_428b7a4e049d1d41fcebaa026fcd138b","title":"Handling Class Imbalance","order":3},{"id":"seed_ef87b51f42063bf9d403fdf614938ce3","title":"Production Deployment","order":4}]},{"id":"seed_ac4e81c68db8fe1d9ac89997df18a501","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Classification Metrics","order":1,"contentType":"LEARNING","contentSlug":"classification-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_eb79d19a52a1cef8a8a8cdd2426f916e","title":"Confusion Matrix","order":0},{"id":"seed_56c6db1f8bc543fa0e141e4e0b025ab8","title":"Accuracy and its Limitations","order":1},{"id":"seed_b23510959d7f2a185c7b18e3741c0e3f","title":"Precision and Recall","order":2},{"id":"seed_b2492dc4263da4b3287d9816c6063289","title":"F1 and F-beta Scores","order":3},{"id":"seed_5d4b7593b2bca1a772cfb84582db8f7e","title":"Specificity and Sensitivity","order":4}]},{"id":"seed_c5ef7edae79ba6a58e3fd0d44017ac94","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"ROC and Precision-Recall Curves","order":2,"contentType":"LEARNING","contentSlug":"roc-and-precision-recall-curves","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f336d1c844076978572127851b8365a4","title":"ROC Curve Construction","order":0},{"id":"seed_6c185b4df6bbfc5861624f6a99c35d55","title":"AUC Interpretation","order":1},{"id":"seed_c8833f1a7cd747239aaaefe2d034f66b","title":"Precision-Recall Curves","order":2},{"id":"seed_dd54720bb9d55472ee7eb3c9d050c699","title":"Average Precision","order":3},{"id":"seed_21b6caddc825af8a012d9a429ef99953","title":"Curve Comparison","order":4}]},{"id":"seed_7e80b225bee84e82f8ac145be8ea9c03","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Multi-class Classification Metrics","order":3,"contentType":"LEARNING","contentSlug":"multi-class-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_76d1743edf649c1ef84825ac26b4c856","title":"Micro vs Macro Averaging","order":0},{"id":"seed_f498477cafa16cc6d7648ac6982793a1","title":"Weighted Averaging","order":1},{"id":"seed_629c2a58a1666636cabb0d6f0f31c9c4","title":"Cohen's Kappa","order":2},{"id":"seed_014845c4745e9003c80cc069f6615ba7","title":"Multi-class Confusion Matrix","order":3},{"id":"seed_e812f4c29eb13b5f0e5be48c5b12003c","title":"One-vs-Rest Metrics","order":4}]},{"id":"seed_4d46bb09037b0706babb1caba43f8b9d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Regression Metrics","order":4,"contentType":"LEARNING","contentSlug":"regression-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ff948bafce2f5d7edf0cb72789b7b84e","title":"Mean Squared Error","order":0},{"id":"seed_54ea2f6fa589871cf4c5992834e38ffd","title":"Mean Absolute Error","order":1},{"id":"seed_b226c87c236216b00dbd2e1dd17e2e14","title":"R-squared and Adjusted R²","order":2},{"id":"seed_86e6589094d7fbf4e3a28dbcfbcbf734","title":"MAPE and SMAPE","order":3},{"id":"seed_7e5d0a4572c4bfe5f7bb65507738a911","title":"Median Errors","order":4}]},{"id":"seed_43ad46d64dbce2d1516e1fb067ceaf9a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Ranking Metrics","order":5,"contentType":"LEARNING","contentSlug":"ranking-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_84f6b9a7d3fb2640cd307846c84b0d3d","title":"Precision@k and Recall@k","order":0},{"id":"seed_221dabfb9032e726242711c9a617c9d8","title":"Mean Average Precision","order":1},{"id":"seed_66b42dd4453f588606e8bb44f8208610","title":"Normalized Discounted Cumulative Gain (NDCG)","order":2},{"id":"seed_c66f91f7f3c243dc416a982d3e5ebea2","title":"Mean Reciprocal Rank","order":3},{"id":"seed_8c99d7299835e38dda06ea4fab4fd2a8","title":"Partial AUC (pAUC)","order":4}]},{"id":"seed_817f0073a4cafb13f8f380c749e23c80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Custom and Business Metrics","order":6,"contentType":"LEARNING","contentSlug":"custom-and-business-metrics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3751bbcee6cb0b764da38c971ea6ee9c","title":"Cost-Sensitive Evaluation","order":0},{"id":"seed_560e5e44206a74694e8bea896fe27329","title":"Threshold Optimization","order":1},{"id":"seed_a60752395d179471ef3a0f088943fbb9","title":"Business Metric Alignment","order":2},{"id":"seed_f60b124657f70409c9629164a01d6a28","title":"Multi-objective Evaluation","order":3},{"id":"seed_21b19afeef2489bc3382cb5a3da77f9f","title":"Metric Selection Strategy","order":4}]},{"id":"seed_d59e5e1e4d128d743b25c62154ab3fbf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 18: Model Evaluation Metrics","sectionOrder":25,"title":"Softmax Probability Distribution","order":7,"contentType":"PROBLEM","contentSlug":"softmax-probability-distribution","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_347487ee43f17af456b511a19a40dca7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Holdout Validation","order":1,"contentType":"LEARNING","contentSlug":"holdout-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_96f58974343fea0b99df7e45d06569f4","title":"Train-Test Split","order":0},{"id":"seed_79ce601fb6fb7e631a58b96376a8bebf","title":"Validation Set","order":1},{"id":"seed_996447f4b323ec6f15878c037211c300","title":"Stratification","order":2},{"id":"seed_9d1b394b638941f99e49558d9d436181","title":"Random Seeds","order":3},{"id":"seed_6ce67226b5c4b9cccbbc93c6f529015b","title":"Limitations","order":4}]},{"id":"seed_e0201302cbabe1c8ac59d7eb9fd7b3b5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Stratified and Group Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"stratified-and-group-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d4926b31a50af824bf28a2e12e0f9e51","title":"Stratified K-Fold Cross-Validation","order":0},{"id":"seed_364fdc1c9f8ff7fb22150a9d7c3a4c96","title":"Group K-Fold Cross-Validation","order":1},{"id":"seed_186dc21adde3df5ac469dfc659c16c21","title":"Leave-One-Group-Out Cross-Validation","order":2},{"id":"seed_69767da960f2ef1284e12f7d69c07111","title":"Grouped Stratification","order":3},{"id":"seed_aeec593746900035c25f344de4612889","title":"Data Leakage Prevention","order":4}]},{"id":"seed_5817575eae860da4b31aff4fbbe86bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation And Resampling","sectionOrder":26,"title":"Bootstrap Methods","order":3,"contentType":"LEARNING","contentSlug":"bootstrap-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9fceb4e5cc593883fca0f5f9c75e086a","title":"Bootstrap Resampling","order":0},{"id":"seed_b37adf0dd1807da5b4259cabf9cd93b4","title":"The .632 Bootstrap Estimator","order":1},{"id":"seed_298028604136134913e79b0b2a571816","title":"The .632+ Bootstrap Estimator","order":2},{"id":"seed_05835f610bc7e373701b43d84b574e21","title":"Out-of-Bootstrap Error","order":3},{"id":"seed_b8da235d9e7370cedf8d0c0b3f04a46c","title":"Bootstrap Confidence Intervals","order":4}]},{"id":"seed_0a5077e15e4a5548c820847a82b9c107","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"K-Fold Cross-Validation","order":1,"contentType":"LEARNING","contentSlug":"k-fold-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4a5fa508a5702be89513ce945d89b128","title":"K-Fold Procedure","order":0},{"id":"seed_7e78d108e68d55ea1e174eef9123547e","title":"Bias-Variance Tradeoff in Cross-Validation","order":1},{"id":"seed_cdae615f0ebafe7ed72873f5c0046608","title":"Choosing K","order":2},{"id":"seed_adaf5c84caed1e7f53c0ef02bbfa4ba3","title":"Repeated Cross-Validation","order":3},{"id":"seed_2d6a6d9e303f07f3098a304cef3c07ef","title":"Confidence Intervals","order":4}]},{"id":"seed_80bb08d6076b67b40673dbd30ce30996","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Time Series Cross-Validation","order":2,"contentType":"LEARNING","contentSlug":"time-series-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c96bf1c05d2725ec3d24f7f73d4cafe2","title":"Forward Chaining","order":0},{"id":"seed_8908ba532db02160ad9a7166a71e9723","title":"Sliding Window Cross-Validation","order":1},{"id":"seed_b2cd7b69fc0fb3be06564ab70e878e37","title":"Expanding Window Cross-Validation","order":2},{"id":"seed_870c537e8969d11394dae89743483b75","title":"Embargo Periods in Time Series Cross-Validation","order":3},{"id":"seed_b752a005c18c1ae028fcf9dbf5849938","title":"Purging and Embargo: Complete Leakage Prevention","order":4}]},{"id":"seed_4caea31c12e70b94b3449288ee507316","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Nested Cross-Validation","order":3,"contentType":"LEARNING","contentSlug":"nested-cross-validation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c869f65db45ee86e76fa7daa0815fb55","title":"Model Selection Bias","order":0},{"id":"seed_3d35bdc7ab663734b82644477f2c49b0","title":"Inner and Outer Loops","order":1},{"id":"seed_1ccc0553096bdced92e308c72e75f469","title":"Unbiased Performance Estimation","order":2},{"id":"seed_e91b35fae33bd40610ac6ca2e1240e3f","title":"Computational Cost","order":3},{"id":"seed_dcdade91f4793997990cbd8f185aeeec","title":"When to Use Nested CV","order":4}]},{"id":"seed_c39b44e15b86f86fb46fad967dc035f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 19: Cross Validation Resampling","sectionOrder":27,"title":"Data Partitioning with Cross-Validation Splits","order":4,"contentType":"PROBLEM","contentSlug":"data-partitioning-cross-validation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_a7879f98db25ce0269f64ef3f5fe4bed","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Hyperparameter Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"hyperparameter-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c1727a878fd60a753256e7ffaecec1a7","title":"Model vs Hyperparameters","order":0},{"id":"seed_ae608fd9f33bc5ddcb24d24ff4fcbae6","title":"Search Space Definition","order":1},{"id":"seed_ff9d725b4d4a85fa6dedd91cd9ca7398","title":"Continuous vs Discrete Hyperparameters","order":2},{"id":"seed_5ae97fec603191f7b453a32729c14de9","title":"Conditional Hyperparameters","order":3},{"id":"seed_de0e7c2c2fb503a7450f39bf0f06abfc","title":"Hyperparameter Importance","order":4}]},{"id":"seed_94e0b48f0fb220a06f9766ad9d913f80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Grid Search","order":2,"contentType":"LEARNING","contentSlug":"grid-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6209f17483fc1db5ff70ef7f333eeba0","title":"Exhaustive Search","order":0},{"id":"seed_c874efee6a2b210b72bdc030260cdaf4","title":"Computational Cost","order":1},{"id":"seed_e37f5215b680d17a0c9cea1d6125eb21","title":"Curse of Dimensionality","order":2},{"id":"seed_cbad045ce9b80711bfe55509f5e22f74","title":"Practical Guidelines","order":3},{"id":"seed_f12c9ef59361fc98cb0f7e75794af995","title":"When Grid Search Works","order":4}]},{"id":"seed_b3ebbda137c88b6799101f8804197d6d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Random Search","order":3,"contentType":"LEARNING","contentSlug":"random-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b8dbd02d376b936535d1107ec2d95803","title":"Random Sampling","order":0},{"id":"seed_9b13f5f0943eebb698f67d8d64ccc3ee","title":"Why Random Beats Grid","order":1},{"id":"seed_e2d85c7a5e32f8659ba80d3d291c93d5","title":"Coverage Probability","order":2},{"id":"seed_56890884b5e61e26e9b61fe35c25d7a6","title":"Practical Advantages","order":3},{"id":"seed_20028fce4f2027a0c041515776790a47","title":"Parallelization","order":4}]},{"id":"seed_eb2d1d9b92ee8ca33315169f186c57a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Bayesian Optimization","order":4,"contentType":"LEARNING","contentSlug":"bayesian-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a66599dc40fa860a585f65cd5071eab0","title":"Sequential Model-Based Optimization","order":0},{"id":"seed_47b354e8d6cc19a442bc7125d76ada91","title":"Surrogate Models: Gaussian Processes","order":1},{"id":"seed_04d556ba4dbf7d1a640ccc10011464b5","title":"Acquisition Functions","order":2},{"id":"seed_bc42f88421c3b49d91e9b742d9c14e07","title":"Exploration-Exploitation Tradeoff","order":3},{"id":"seed_6bc328fe1353478592e63c06564c3e2b","title":"Tree-structured Parzen Estimators (TPE)","order":4}]},{"id":"seed_217594c65573968f0c532e09b11d0155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Multi-Fidelity Optimization","order":5,"contentType":"LEARNING","contentSlug":"multi-fidelity-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_f1dd2aefb4623e0f136b03d1a9fd468e","title":"Early Stopping Approaches","order":0},{"id":"seed_73657a73228ebc847985937d21fd986b","title":"Successive Halving","order":1},{"id":"seed_bd255231e218ccc5d18f5638e4c50b0d","title":"Hyperband","order":2},{"id":"seed_f4a024c7b2a88330c0f12654003dbff7","title":"BOHB: Bayesian Optimization and Hyperband","order":3},{"id":"seed_ab7e60ffac3c3caaf774ec88eed94491","title":"Budget Allocation","order":4}]},{"id":"seed_cd56aef607f08e69d72c3e7d013c7a26","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 20: Hyperparameter Optimization","sectionOrder":28,"title":"Advanced HPO Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-hpo-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ca22bfaaef6ad00aca2a8d0e34a0ab40","title":"Neural Architecture Search","order":0},{"id":"seed_009bd7c79344b542aa9a674509f6c02c","title":"Meta-Learning for HPO","order":1},{"id":"seed_1cbece4390cf0830651828b99278c979","title":"Transfer HPO","order":2},{"id":"seed_ea8fcd74956aac3f7e319e66725b90e9","title":"Multi-Objective HPO","order":3},{"id":"seed_9f46eb66582c82266b5ef05b90678c14","title":"Practical HPO Systems","order":4}]},{"id":"seed_dfc3625133e375f2ddb7281b904bc083","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Problem Formulation","order":1,"contentType":"LEARNING","contentSlug":"clustering-problem-formulation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b089f19bab925ede1a5a7f526be2e789","title":"Clustering Objectives","order":0},{"id":"seed_5304cef754736ff2c73bf9e8874fa25b","title":"Similarity and Distance","order":1},{"id":"seed_1615fd35e02428f5d2c5a573e26ab35c","title":"Cluster Definitions","order":2},{"id":"seed_31f34316ffaa4b3baf2fa63656a370f6","title":"Types of Clustering","order":3},{"id":"seed_f062a33054e42f50b4f51c6adbc7ed1e","title":"Evaluation Challenges","order":4}]},{"id":"seed_677d6ef0d11ced3abb40fe90153268f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Clustering","order":2,"contentType":"LEARNING","contentSlug":"k-means-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1e3f708a5168d3548eb39e633bfa7a1d","title":"Lloyd's Algorithm","order":0},{"id":"seed_a4a8bdb288e0f48b56be276f51be91a8","title":"Objective Function","order":1},{"id":"seed_89c17964401784133112986793dda6a7","title":"Initialization Strategies (K-Means++)","order":2},{"id":"seed_c10fb36d3e93c249e4720cc30f5b1e44","title":"Convergence Properties","order":3},{"id":"seed_4f8b3d4dfcb5421805762e0aa52f868f","title":"Limitations","order":4}]},{"id":"seed_d3f71756678ae1cb6211450c7d2ca5ba","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"K-Means Variants: Beyond Standard K-Means Clustering","order":3,"contentType":"LEARNING","contentSlug":"k-means-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ecfd11f888404c7f61b0bbd5ff07640e","title":"K-medoids (PAM): Robust Clustering with Actual Data Points","order":0},{"id":"seed_2e01fc478d63e0ddaee892059d751b3d","title":"K-medians: Robust Clustering with L1 Distance","order":1},{"id":"seed_9ae17bff6520c9a70120ad5f163f6171","title":"Fuzzy C-means: Soft Clustering with Degrees of Membership","order":2},{"id":"seed_801a5b873b7725088678a6f4d8143249","title":"Mini-batch K-means: Scalable Clustering for Large Datasets","order":3},{"id":"seed_c9bb3c0be14c9a44a5b9fc642f979f62","title":"Bisecting K-means: Divisive Hierarchical Clustering","order":4}]},{"id":"seed_ad45ea7d5ec46859780dfb1e1b634e18","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Hierarchical Clustering","order":4,"contentType":"LEARNING","contentSlug":"hierarchical-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_189f0ea3bae4dd143d60d114fb060eca","title":"Agglomerative Approach","order":0},{"id":"seed_6a63babe42c42f6665c309fcc31ba64e","title":"Linkage Methods","order":1},{"id":"seed_4abf6067754d8c0fcadb906019fb9c1d","title":"Dendrogram Interpretation","order":2},{"id":"seed_a27cc935feaa648420227f59247cb744","title":"Cluster Extraction","order":3},{"id":"seed_29e5fee5d435cfa9c93f6e30017918e9","title":"Divisive Clustering","order":4}]},{"id":"seed_781388f8e96bee94ce065ff1db0c0064","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Spectral Clustering","order":5,"contentType":"LEARNING","contentSlug":"spectral-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ec02b9c7972c72124ee15be7ce7d8ad","title":"Graph-based Formulation","order":0},{"id":"seed_42ed0ee73260805bdbaf532293ad8d09","title":"Laplacian Matrices","order":1},{"id":"seed_9be9cf14470207f3f93da2acd71563ae","title":"Eigenstructure","order":2},{"id":"seed_298e69f87a81c3eaf9b76c67654e4574","title":"NCut Objective","order":3},{"id":"seed_a5e6b4e0cbe1f8d2eda0b37b1651c113","title":"Algorithm and Limitations","order":4}]},{"id":"seed_d758ff45163e4d63f66bd744695a183b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Clustering Evaluation","order":6,"contentType":"LEARNING","contentSlug":"clustering-evaluation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1dba3add7d890e7ed2fd9c537f280c45","title":"Internal Metrics: Silhouette and Calinski-Harabasz","order":0},{"id":"seed_c8b84931667d930903c024296f164e2a","title":"External Metrics: Adjusted Rand Index and Normalized Mutual Information","order":1},{"id":"seed_437524f646839aeb80265ae9fe9686e0","title":"Stability-Based Evaluation: Assessing Cluster Robustness","order":2},{"id":"seed_2e2e2d67723dec45f5a53a0d4d1e9be2","title":"Gap Statistic: Comparing Clusters to Null References","order":3},{"id":"seed_3cf56bbb1b170bcc21e7c1ea90f075d1","title":"Selecting the Number of Clusters: A Comprehensive Framework","order":4}]},{"id":"seed_14546b73b6439e5fd290e7eac815870c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":7,"contentType":"VISUALIZER","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_11791ef708ec1c800feac411b9252ab8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 21: Clustering Algorithms","sectionOrder":29,"title":"Centroid-Based Data Partitioning","order":8,"contentType":"PROBLEM","contentSlug":"centroid-based-partitioning","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_68bdb028486489eb91f411607b9412b6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Density Estimation Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"density-estimation-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bbb5eca0fbcddc97a6984f2769ecc5c7","title":"Parametric vs Nonparametric Density Estimation","order":0},{"id":"seed_edd346fe05ab0177ed966a216f2fa23e","title":"Histogram Estimation","order":1},{"id":"seed_a82348a2647767f50e7ab3561b82c50b","title":"Kernel Density Estimation","order":2},{"id":"seed_60087af46b4e2f79831010f3bab28e1d","title":"Bandwidth Selection","order":3},{"id":"seed_78a5949540ebd7773826f892dcc520bb","title":"Multivariate Density Estimation","order":4}]},{"id":"seed_64066580a6eb406e58043cabb534f9da","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Kernel Density Estimation Theory","order":2,"contentType":"LEARNING","contentSlug":"kde-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_107fbc16a9928259ae66c5cb063a0707","title":"Kernel Functions","order":0},{"id":"seed_c3f4dad9ae7636c16b96c906a1ede04e","title":"Bias-Variance Tradeoff","order":1},{"id":"seed_c4447a6011b5b256bc1280309125ea13","title":"Bandwidth Selection Methods","order":2},{"id":"seed_c6395d360e39986339808328c2cdf376","title":"Curse of Dimensionality","order":3},{"id":"seed_089dbc641086db4da095e378a3d3de5e","title":"Variable Bandwidth","order":4}]},{"id":"seed_a4234b761eb1944905dac7fc3a64bf7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation And Mixture Models","sectionOrder":30,"title":"Gaussian Mixture Models","order":3,"contentType":"LEARNING","contentSlug":"gaussian-mixture-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6b3392112aba299b27c333d1879af50e","title":"Mixture Model Formulation","order":0},{"id":"seed_b159d44cf189a2d2f4c20208788cdac3","title":"Latent Variables","order":1},{"id":"seed_39f034a068e36dceed9c15c9c28abf63","title":"Marginal Likelihood","order":2},{"id":"seed_efc323d98975bf9a7ba16cc9928626f4","title":"Identifiability","order":3},{"id":"seed_cec33a366a8e47f2795f023698a401ce","title":"Model Selection","order":4}]},{"id":"seed_a2eacc9551297ba9d8a48167d5c81781","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"The Expectation-Maximization Algorithm","order":1,"contentType":"LEARNING","contentSlug":"expectation-maximization-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bbb1b3beed576c08ddbec3105e02b929","title":"EM for GMMs: Maximum Likelihood with Latent Variables","order":0},{"id":"seed_333a9128f075c97486db7491a457a1cb","title":"E-step and M-step: Complete Mathematical Derivation","order":1},{"id":"seed_dcb02b0cf3fae1925c138a93b3cc1c34","title":"Convergence Properties of the EM Algorithm","order":2},{"id":"seed_5d62c01c38b40befdb6cc73b6e44197b","title":"Initialization Strategies for EM","order":3},{"id":"seed_9dbbcb99ad730181df3b9b3f9918661a","title":"EM Variants: Extensions and Modifications","order":4}]},{"id":"seed_22d9cdb6f03f9f0e12d8f69cb35600cf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Beyond Gaussian Mixtures","order":2,"contentType":"LEARNING","contentSlug":"beyond-gaussian-mixtures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b969dc87d61eabcadf2d1f183832fe54","title":"Student-t Mixtures","order":0},{"id":"seed_257fa56282ecc9a3b48787514804134e","title":"Mixture of Experts","order":1},{"id":"seed_144e11e2b74b3b8fa3281b06b9d1fe37","title":"Hidden Markov Models","order":2},{"id":"seed_b2d2d0fb3dbbb587e2f27c7739a3384a","title":"Infinite Mixtures (DPMM)","order":3},{"id":"seed_3858952829e79b826e732eac5ad03474","title":"Semi-Parametric Methods","order":4}]},{"id":"seed_8758cd49b4805ac155a60d016a0a2e38","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 22: Density Estimation Mixture Models","sectionOrder":31,"title":"Density-Based Clustering","order":3,"contentType":"LEARNING","contentSlug":"density-based-clustering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ba395c54f9d9ea8ca2eeefd4673a0028","title":"DBSCAN Algorithm","order":0},{"id":"seed_8fe88e5271030d69b551e64982149d60","title":"Core, Border, Noise Points","order":1},{"id":"seed_80be288ec193bdd9cc10f935b1c26dcb","title":"OPTICS Algorithm","order":2},{"id":"seed_f87a7db0e0102125b4b14e9a1f9d0a9b","title":"HDBSCAN","order":3},{"id":"seed_2e84f196bcb34ee0c34f3e7e2df72c0f","title":"Parameter Selection","order":4}]},{"id":"seed_983a6d13254cf67c792c3d012aa231b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Dimensionality Reduction Motivation","order":1,"contentType":"LEARNING","contentSlug":"dimensionality-reduction-motivation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_912e1c10fef59079b6b8269acf3230fd","title":"The Curse of Dimensionality","order":0},{"id":"seed_1ec58bb219d6e84a5e165a50d0124c3c","title":"Visualization","order":1},{"id":"seed_0686dd413b08ac697aeb85ae8cceedfb","title":"Noise Reduction","order":2},{"id":"seed_f2a25a2b1d5b5cec93bf07633721cbad","title":"Compression","order":3},{"id":"seed_15316592975c3f16e775c7ac595d9db8","title":"Feature Extraction vs Selection","order":4}]},{"id":"seed_af0b27842aa6a92042251980b9650ad8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Analysis Theory","order":2,"contentType":"LEARNING","contentSlug":"pca-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_20f7c3d891b0fc52c198c119ae67c687","title":"Maximum Variance Formulation","order":0},{"id":"seed_44e28c613c7e5f6528848566ac51a630","title":"Minimum Reconstruction Error","order":1},{"id":"seed_d1580eba7586e46c2cbd9b839cc6bee3","title":"Eigenvalue Problem","order":2},{"id":"seed_3aa8e145297a38c3e51fa7412fdc4e94","title":"Proportion of Variance Explained","order":3},{"id":"seed_081e9cb5217bdf74943b2959c53868ed","title":"Scree Plots","order":4}]},{"id":"seed_0ae7eac699c5b7e0ea73d5f865fb72b9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"PCA Variants","order":3,"contentType":"LEARNING","contentSlug":"pca-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ac9557094f991ff3a4042e12b6e0fada","title":"Standardized PCA","order":0},{"id":"seed_347ca9323de8f50d3285ede6621b14a3","title":"Weighted PCA","order":1},{"id":"seed_71b848987c7f843856ff7cd0f50d8974","title":"Robust PCA","order":2},{"id":"seed_d23b347e5521402c480e59649f653973","title":"Sparse PCA","order":3},{"id":"seed_0d0967c7364654ec49d6b109ad9b3467","title":"Incremental PCA","order":4}]},{"id":"seed_d09ca8e44e15e4da659e7b542cc04446","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Kernel PCA","order":4,"contentType":"LEARNING","contentSlug":"kernel-pca","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fd272f8d4db44a9ca2cbfc8360148ffb","title":"Nonlinear Dimensionality Reduction","order":0},{"id":"seed_5400ef79a7f070f169027bd79a365f88","title":"Kernel Trick for PCA","order":1},{"id":"seed_7f2388689534f45d7ba5f46954a27e6a","title":"Centering in Feature Space","order":2},{"id":"seed_9860cb79b866ce7f880c867613e8b08a","title":"Pre-image Problem","order":3},{"id":"seed_3300acc44a0cf2017421222e7c9d15ee","title":"Kernel Selection","order":4}]},{"id":"seed_85afe3c0f35cb0210def66038cb352a2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Factor Analysis","order":5,"contentType":"LEARNING","contentSlug":"factor-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7bbdf7b48897b13c4fb8043fd4fa5f44","title":"Latent Factor Model","order":0},{"id":"seed_bce8abd653715f00413fe0662adac352","title":"Factor Analysis vs PCA","order":1},{"id":"seed_728f45bd9ac266cb008a76469ae821d1","title":"Factor Rotation","order":2},{"id":"seed_c8489c65a0f15ed44c442c3a39e43045","title":"Maximum Likelihood Estimation","order":3},{"id":"seed_0ef3d85aa0a970115ffe801fcc063ab5","title":"Interpretation","order":4}]},{"id":"seed_ab73773fa16c6ed214ba79504d07c444","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Independent Component Analysis","order":6,"contentType":"LEARNING","contentSlug":"independent-component-analysis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c953d9d671e2834e5df5d31926fe26d","title":"The ICA Model","order":0},{"id":"seed_baf84ad7fa2165a7b18b802020a41511","title":"Non-Gaussianity","order":1},{"id":"seed_90cbf41d8f4a8c190e4f5568f9e72087","title":"FastICA Algorithm","order":2},{"id":"seed_211454791a8f15cc2083aba2251eb423","title":"Applications: Blind Source Separation","order":3},{"id":"seed_cfcbc153c5760cc4e28169716c306f99","title":"ICA vs PCA","order":4}]},{"id":"seed_c61ed49082d6e5a2589810425c9b31f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":7,"contentType":"VISUALIZER","contentSlug":"principal-component-extraction","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_01b5934d7f8ef0bf58ff57e63421b50f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 23: Dimensionality Reduction","sectionOrder":32,"title":"Principal Component Extraction","order":8,"contentType":"PROBLEM","contentSlug":"principal-component-extraction","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_1c389cf030159b7da28f11969faaa3b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Manifold Hypothesis","order":1,"contentType":"LEARNING","contentSlug":"manifold-hypothesis","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a46b438d566eb49621532fee820bad5b","title":"Manifold Definition","order":0},{"id":"seed_2c3f99ff1a35dde9b0c74c53a5742d29","title":"Intrinsic Dimensionality","order":1},{"id":"seed_0125cda1e7dda4017dc1215329c9bd74","title":"Manifold Examples","order":2},{"id":"seed_f0689c63aba5b0afa145a1be17182695","title":"Implications for Machine Learning","order":3},{"id":"seed_50733b5adaf78fbc0e9daabc9556c471","title":"Estimation Approaches","order":4}]},{"id":"seed_c1c7cd66ec0fed623f6f8f579b7062f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Multidimensional Scaling","order":2,"contentType":"LEARNING","contentSlug":"multidimensional-scaling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_53378b9ca349c331c069fc27ae7994eb","title":"Classical Multidimensional Scaling","order":0},{"id":"seed_aef6798750aad0d0539a4586f9896be5","title":"Metric Multidimensional Scaling","order":1},{"id":"seed_fe5447f3f28894b6b596ae9b4a795659","title":"Non-Metric Multidimensional Scaling","order":2},{"id":"seed_178344d97b978cffa4d596199c22c33d","title":"Stress Functions","order":3},{"id":"seed_c52fd77f8bf0aaeac21c2e1ddaf31605","title":"Limitations and Practical Considerations","order":4}]},{"id":"seed_71ecb495764198e1daba7a26711139fc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Isomap: Isometric Feature Mapping","order":3,"contentType":"LEARNING","contentSlug":"isomap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dfb6c8adf4eda3baa5ade9688ab42b1b","title":"Geodesic Distances: The Foundation of Isomap","order":0},{"id":"seed_12f15492860258793fd64c99e330f744","title":"Graph Construction: Building the Neighborhood Network","order":1},{"id":"seed_e870cfb1d05a8fcc008a1062641deca6","title":"Landmark Isomap: Scaling to Large Datasets","order":2},{"id":"seed_11b5778ab48d85fdf0607f9ca9b1d893","title":"Assumptions and Limitations: When Isomap Fails","order":3},{"id":"seed_3834689ea1d632e8a62d9657f4b0653e","title":"Isomap Applications: From Theory to Practice","order":4}]},{"id":"seed_73855f5207a19a961f18fcd49f63f1a4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"Locally Linear Embedding","order":4,"contentType":"LEARNING","contentSlug":"locally-linear-embedding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1fc4740d0c790d5908b98a8a73724f2c","title":"Local Linear Approximation","order":0},{"id":"seed_a6abe1791ca23c5526b2b051b0fdbdf1","title":"Weight Computation","order":1},{"id":"seed_21e47f52959ddab8535587dbe00ad204","title":"Embedding Optimization","order":2},{"id":"seed_1800071eccb52453aa7157de2bcbb523","title":"LLE Variants","order":3},{"id":"seed_73e541dd02b7a69a4a00e0c5bb0206ab","title":"Limitations","order":4}]},{"id":"seed_492a583a08bf6d335d7e2a481be94363","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"t-Distributed Stochastic Neighbor Embedding (t-SNE)","order":5,"contentType":"LEARNING","contentSlug":"t-sne","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_02ee1a862967d4ac401771d806d0929c","title":"The t-SNE Objective Function","order":0},{"id":"seed_3ac0c6acd4158516e2912980d02667bc","title":"The Perplexity Parameter","order":1},{"id":"seed_438161193705ce8795fee589e1bcee09","title":"Optimization Challenges in t-SNE","order":2},{"id":"seed_c724e618bd1c800c0d9eea020e962bae","title":"Interpretation Guidelines for t-SNE","order":3},{"id":"seed_c8e0477fe4e0e28cca9e31787a78a416","title":"Common Pitfalls in t-SNE Usage","order":4}]},{"id":"seed_605375d1d6334b96f0eead774cc314f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 24: Manifold Learning","sectionOrder":33,"title":"UMAP (Uniform Manifold Approximation and Projection)","order":6,"contentType":"LEARNING","contentSlug":"umap","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bb63f27779bef21990bf2ac13110b96e","title":"UMAP Theory","order":0},{"id":"seed_0091f8f64a0658497d67b968f6af6234","title":"Fuzzy Topological Representation","order":1},{"id":"seed_9bc5103a3f5ada83399a850f534b910b","title":"Cross-Entropy Optimization","order":2},{"id":"seed_05f04e22aa8a260bc0a7afbc62fc4859","title":"UMAP vs t-SNE","order":3},{"id":"seed_cd00d983d9ffd0ae94b467f53318e936","title":"Hyperparameter Tuning","order":4}]},{"id":"seed_bff5e7a0bc4fddf9a78ccd4982e6832d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Anomaly Detection Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"anomaly-detection-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a75f2514b43378710922e7e504567ab0","title":"Anomaly Types","order":0},{"id":"seed_37a380ea9282f31012aadbfe5badbfab","title":"Point vs Contextual vs Collective Anomalies","order":1},{"id":"seed_fb4533eb754db059404e5004dbe3e66d","title":"Supervised vs Unsupervised Anomaly Detection","order":2},{"id":"seed_f96a62d15c2a13293059eb100a888ca8","title":"Evaluation Challenges","order":3},{"id":"seed_158a60c6bf2c35aa5db6a14a6999f4a0","title":"Applications of Anomaly Detection","order":4}]},{"id":"seed_a2dd0d8bf5ebd6be590cd9d205ca3b5a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Distance-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"distance-based-methods","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4640896fdeb02b116edc122b4bcd5a4b","title":"KNN-Based Detection","order":0},{"id":"seed_0b909907eae74e35c96604ef032928b7","title":"Mean Distance to KNN","order":1},{"id":"seed_d92f381abd957a4a5e9d869239cf4e22","title":"Local Outlier Factor (LOF)","order":2},{"id":"seed_cfdf97f3c7eba77f317fa550721b480d","title":"LOCI Algorithm","order":3},{"id":"seed_abdaa7e995947a05d5e9b2a6b643e26d","title":"Curse of Dimensionality","order":4},{"id":"seed_547ff0a528fd9e11daeb22afe34ec216","title":"Parameter Selection","order":5}]},{"id":"seed_1a47f9a533ee25611d5c267af621d678","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly And Outlier Detection","sectionOrder":34,"title":"Isolation Forest for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"isolation-forest","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_902eaf1bd0ed7c1e4a09f48c2bec1385","title":"Isolation Principle","order":0},{"id":"seed_f8ee0f72d0e08392e7f4beee0611fbca","title":"Random Partitioning","order":1},{"id":"seed_f5676bde047dc12d1068b5bf264107cf","title":"Path Length Scoring","order":2},{"id":"seed_6922d496d0b96961aea46aded9a46697","title":"Extended Isolation Forest","order":3},{"id":"seed_bfab2b5d79c8694a742e836a160225ca","title":"Parameter Selection","order":4}]},{"id":"seed_76b018ba7d1f8fc744e0978c016eb04e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Statistical Methods for Anomaly Detection","order":1,"contentType":"LEARNING","contentSlug":"statistical-methods-anomaly-detection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c9f85db4c96fc707e369f4d1f2755cb3","title":"Z-Score Method","order":0},{"id":"seed_6acc6263b1ae1202d4420689e8d6407b","title":"IQR Method","order":1},{"id":"seed_f1a4ca66ce7582c6c382b784e556e716","title":"Grubbs' Test","order":2},{"id":"seed_d7fdc195fa435af05d9d03c56e62464a","title":"Multivariate Methods","order":3},{"id":"seed_213b2c173b065677456f7f16df65678c","title":"Assumptions and Limitations","order":4}]},{"id":"seed_a8fc19bd78ba2746be4fd62a27ece203","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"Density-Based Methods for Anomaly Detection","order":2,"contentType":"LEARNING","contentSlug":"density-based-methods-anomaly","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6b56e8b61131d3e8af54c2fcc7856cc4","title":"Relative Density","order":0},{"id":"seed_a9732a42f76251cab3d402316568935c","title":"DBSCAN for Outliers","order":1},{"id":"seed_96a7b0650b1eb4e7d19fce9166c6f670","title":"Local Density Estimation","order":2},{"id":"seed_b281a0f18493953e1ad791947c064d13","title":"Multivariate Density","order":3},{"id":"seed_205d0860a66609a6de9e79b132ac3d2f","title":"Scalability","order":4}]},{"id":"seed_4555f92571404920aa7a0ff10edd4942","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 25: Anomaly Outlier Detection","sectionOrder":35,"title":"One-Class Methods for Anomaly Detection","order":3,"contentType":"LEARNING","contentSlug":"one-class-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_33cd6a3e2cceee8f2f591bcb6eb54095","title":"One-Class SVM","order":0},{"id":"seed_b8078809723ff37f83ee93969145abd3","title":"Support Vector Data Description (SVDD)","order":1},{"id":"seed_0a7d5b9c468e68d8d129b6ad748de07e","title":"Autoencoders for Anomaly Detection","order":2},{"id":"seed_355ed88cbc012cdd90302a96ebb703f3","title":"Neural Network Approaches","order":3},{"id":"seed_bb883f6dcf92c98848973bff66f40afd","title":"Threshold Selection","order":4}]},{"id":"seed_ef80465133d1fe9ce84bf4ab98e19dea","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"The Bayesian Framework","order":1,"contentType":"LEARNING","contentSlug":"bayesian-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_57ede80a0dfb4eed251ce8a2f694b3db","title":"Prior Beliefs","order":0},{"id":"seed_e66479a72e4cd01a68d6b738dedd9c04","title":"Likelihood Function","order":1},{"id":"seed_ddb54dd0f1518648084444acf584ffc8","title":"Posterior Distribution","order":2},{"id":"seed_f9332f3a80481143525c0f6cef36fc02","title":"Bayesian Updating","order":3},{"id":"seed_4c7ae9de131d8e972a26cba41a38bb32","title":"Point Estimates from Posterior","order":4}]},{"id":"seed_e20205c549adb1a21e00c02f2724d38b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Conjugate Priors","order":2,"contentType":"LEARNING","contentSlug":"conjugate-priors","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c7ef3ffd1936a8a7a3bfba1bf8fba047","title":"Conjugacy Definition","order":0},{"id":"seed_9c253446fb8b056786d2a3ae3dd1f157","title":"Beta-Binomial","order":1},{"id":"seed_8fe1d086ccf4ea874c725c13bd41b140","title":"Gaussian-Gaussian","order":2},{"id":"seed_1687234d9f67927cc75c4d671ae819a1","title":"Dirichlet-Multinomial","order":3},{"id":"seed_a13f1a1e8dcf99041113a499b3abb3f6","title":"Practical Implications","order":4}]},{"id":"seed_b347cdec69ff8713db8903d02f3d40cd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Linear Regression","order":3,"contentType":"LEARNING","contentSlug":"bayesian-linear-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c69b6ded351da5baea1b712c3acfc92a","title":"Prior on Weights","order":0},{"id":"seed_bc9c8107cfe67b4e064606e05c7bf126","title":"Posterior Derivation","order":1},{"id":"seed_bb66c141e2459b5b8c64af093e3c8a1b","title":"Predictive Distribution","order":2},{"id":"seed_0b31eaeaf054332560df761f0d2d5700","title":"Uncertainty Quantification","order":3},{"id":"seed_885657b641e84a3736c9940cd562b6a6","title":"Connection to Regularization","order":4}]},{"id":"seed_017d50d5c031b5e4d0e1445faf6ce982","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Bayesian Model Comparison","order":4,"contentType":"LEARNING","contentSlug":"bayesian-model-comparison","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_22814dfeab80e59d04cc7c49d2e19dc1","title":"Marginal Likelihood: The Foundation of Bayesian Model Comparison","order":0},{"id":"seed_67d4acb9b96d5eac0f02ae5aa9b54f6b","title":"Bayes Factors: Quantifying Relative Model Evidence","order":1},{"id":"seed_08c71e635e6cd0a1352668348196e974","title":"Model Evidence: Understanding What Marginal Likelihood Measures","order":2},{"id":"seed_ed77ac4d24834fbfa5e84f9c8befc377","title":"Occam's Razor: The Bayesian Preference for Simplicity","order":3},{"id":"seed_110151d16421a7ad3179fe5adb3ff049","title":"Bayesian Model Averaging: Combining Models for Robust Predictions","order":4}]},{"id":"seed_d310f58ff165b49c4ec608aa0227cacf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Sampling Methods","order":5,"contentType":"LEARNING","contentSlug":"approximate-inference-sampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3f76f0d410a2c416feb5ad6f0dcce367","title":"Monte Carlo Methods","order":0},{"id":"seed_ddf30f65b6b041be44012fc07f4e9bc5","title":"Rejection Sampling","order":1},{"id":"seed_21c68122d9f12730912d7935be8dc47c","title":"Importance Sampling","order":2},{"id":"seed_44dc87aa918da2317593cfbe96d9a2db","title":"MCMC: Metropolis-Hastings Algorithm","order":3},{"id":"seed_4cfa9e6e917af2a36a3eb791c98af5c6","title":"Gibbs Sampling","order":4}]},{"id":"seed_5c847f1e908149fa7ac07f86188b319b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Approximate Inference: Deterministic Methods","order":6,"contentType":"LEARNING","contentSlug":"approximate-inference-deterministic","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4e4a1ef0d2f8f5b9aa35295f5758efde","title":"Laplace Approximation","order":0},{"id":"seed_43179d20e9494e54ad0651b10d8bb343","title":"Variational Inference Overview","order":1},{"id":"seed_ac9d0bd70cac5dfde182f6d56fc9b58d","title":"Expectation Propagation","order":2},{"id":"seed_b3330e7480d3df8908296d51e12bd38c","title":"Deterministic vs Stochastic Methods","order":3},{"id":"seed_76eca41d176450df29a491bc99075d8b","title":"Choosing Approximation Method","order":4}]},{"id":"seed_46d5fd4b152d1cd30b9443131d4b0c73","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 26: Bayesian Inference","sectionOrder":36,"title":"Posterior Parameter Estimation with Conjugate Prior","order":7,"contentType":"PROBLEM","contentSlug":"posterior-parameter-estimation-conjugate-prior","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_ca2a8840c6015e1ebbb86e35dbba1513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Probabilistic Graphical Models","order":1,"contentType":"LEARNING","contentSlug":"probabilistic-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8950895aa4622ba05e81dfed077de872","title":"Probabilistic Graphical Models Overview","order":0},{"id":"seed_3094911232a94d940483af401f69d0f3","title":"Directed vs Undirected Graphical Models","order":1},{"id":"seed_e1fc5247b2aa5ed4a91f7284e86e0fb3","title":"Conditional Independence","order":2},{"id":"seed_c9b8d4aa1586eb9f9de47bfb5e9d196c","title":"D-Separation","order":3},{"id":"seed_5702d0208352b81b1733dfeb24b838bd","title":"Markov Blanket","order":4}]},{"id":"seed_f736480433bf6703491b1838f1c22be3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Bayesian Networks","order":2,"contentType":"LEARNING","contentSlug":"bayesian-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5eea4032f85cdfede42c406023f9aad9","title":"DAG Structure","order":0},{"id":"seed_3a5709b9eb7cd043da6d440a1855637b","title":"Factorization","order":1},{"id":"seed_3bd2448933a7395d9c28fbea1d71f008","title":"CPT Representation","order":2},{"id":"seed_b088713137555b3f7fc612a911cffa11","title":"Parameter Learning","order":3},{"id":"seed_f630a5895b3042d8b44098717328ce9c","title":"Structure Learning","order":4}]},{"id":"seed_29f3e6fc90cbda45c1970a76aa59d663","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Markov Random Fields","order":3,"contentType":"LEARNING","contentSlug":"markov-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_af9f586f674bf6e4ac05b4e35f0ce09f","title":"Undirected Graphs","order":0},{"id":"seed_384252b550fc16c66afbfb7afdaddfbb","title":"Potential Functions","order":1},{"id":"seed_e2ae54aafcf657fe7d743e42be5d3dc6","title":"Partition Function","order":2},{"id":"seed_076ec3dde0d456f5ad72ba406bf6849c","title":"Hammersley-Clifford Theorem","order":3},{"id":"seed_3158788ad60c0adbe748369c74a09cf2","title":"Energy-Based Models","order":4}]},{"id":"seed_24b1625575d59d42572164bfa2537fd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Inference in Graphical Models","order":4,"contentType":"LEARNING","contentSlug":"inference-in-graphical-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6aa0bef406691d15c92687d7462ed0e4","title":"Variable Elimination","order":0},{"id":"seed_4f6ac74ee592eafc6f39f3073f20b08d","title":"Belief Propagation","order":1},{"id":"seed_a747577f3daedb808971c7a19ba88dbe","title":"Junction Tree Algorithm","order":2},{"id":"seed_eeb4c61eb86343334c596641bc29fb95","title":"Loopy Belief Propagation","order":3},{"id":"seed_8efca05856e3bd5998dbff33a79e677f","title":"Approximate Inference","order":4}]},{"id":"seed_26906c87473209c81f340c709f82c90c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Hidden Markov Models","order":5,"contentType":"LEARNING","contentSlug":"hidden-markov-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_389a4f87ffb52df887b245e7dc3bfe4f","title":"HMM Structure","order":0},{"id":"seed_9f4eab4243846b1fe3d00bb2f5f53c51","title":"Forward Algorithm","order":1},{"id":"seed_67e0e1596a0b9aed578e7e3f111d2669","title":"Viterbi Algorithm","order":2},{"id":"seed_d7ff6f9584a023158a3644efe626b73d","title":"Baum-Welch (EM) Algorithm","order":3},{"id":"seed_9db56e30a3b5c9efc5f3c3b28583d98e","title":"Applications","order":4}]},{"id":"seed_70298c6e59e6d4aa717b493f4727004f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 27: Graphical Models","sectionOrder":37,"title":"Conditional Random Fields","order":6,"contentType":"LEARNING","contentSlug":"conditional-random-fields","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_542ce9a44ca588c80b65d760302448ca","title":"CRF Formulation","order":0},{"id":"seed_3dffcd1e45d6e120f972fadecbcf1218","title":"Linear-Chain CRF","order":1},{"id":"seed_1582f425ed12cdbea274ec383b79a394","title":"Feature Functions","order":2},{"id":"seed_efed3d7acbc46c26e3de085ddd9f09d6","title":"Training","order":3},{"id":"seed_645f81110acf1d13107c29254ad1a4fd","title":"Sequence Labeling","order":4}]},{"id":"seed_bfcd86cd712852ba44877065af04967f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"gp-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2b7c735cc967ead79dd6b7462d4d95bf","title":"Function Space View","order":0},{"id":"seed_0dabdf2e335aa43cea3e5d3a0c539a03","title":"Weight Space View","order":1},{"id":"seed_c81dc03d368309f71c673c0a5c454d22","title":"GP Definition","order":2},{"id":"seed_fba46d8f9480a1d84200d6c569b78810","title":"Mean and Covariance Functions","order":3},{"id":"seed_2b0dc76792bd09941081ab5b780d88eb","title":"Samples from GP Prior","order":4}]},{"id":"seed_379822a695955a78bd4ea16f38131ea2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Regression","order":2,"contentType":"LEARNING","contentSlug":"gp-regression","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7f94f668ffa7e6ea7cbb42e020fe4458","title":"Posterior Derivation","order":0},{"id":"seed_618ac5edc2b9cfb835d2c73fbbde3a7f","title":"Predictive Distribution","order":1},{"id":"seed_be3486e2b14dad5fdff506115033b705","title":"Mean and Variance Prediction","order":2},{"id":"seed_8dd8643c7e33607673eafc995b0f6ba9","title":"Noise Handling","order":3},{"id":"seed_f3e2d25d5f577fdb91bb880fdfd252eb","title":"Closed-Form Solution","order":4}]},{"id":"seed_9c7afac43216ccc7ff3ab116639b4514","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Kernel Design for Gaussian Processes","order":3,"contentType":"LEARNING","contentSlug":"kernel-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e61c65e0ba4294b4b125adb630d8b9df","title":"Stationary Kernels","order":0},{"id":"seed_b555cf35adb8753ce3ebbb4db869d64c","title":"Non-Stationary Kernels","order":1},{"id":"seed_564223aa6c5ba9bdcd9b4c699aa19c97","title":"Kernel Composition","order":2},{"id":"seed_db6876abceff3ad783fbb93e398b07ac","title":"Spectral Analysis","order":3},{"id":"seed_e59ac9f2a51c8f742467bc6f05785053","title":"Automatic Relevance Determination","order":4}]},{"id":"seed_f84c8f27bdd419c923e2b1700bde6637","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Hyperparameter Learning in Gaussian Processes","order":4,"contentType":"LEARNING","contentSlug":"gp-hyperparameter-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_aa422e8aec77347db1c0897daac12e51","title":"Marginal Likelihood","order":0},{"id":"seed_c5e068969210af7349969b8ce951475d","title":"Gradient Computation","order":1},{"id":"seed_164ab4c219cc79549b51eac06dc68fc1","title":"Optimization Strategies","order":2},{"id":"seed_26eb57dc8bc1cc97d05f842fb702d735","title":"Cross-Validation Alternative","order":3},{"id":"seed_2063bb52833882fc3ff39324344fa26f","title":"Practical Considerations","order":4}]},{"id":"seed_6b381cf713c81aa18f4233121bab820e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Scalable Gaussian Processes","order":5,"contentType":"LEARNING","contentSlug":"scalable-gps","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ec82e73c14c1ad9e4ebf4dcfa4abd6c6","title":"Exact GP Limitations","order":0},{"id":"seed_b2c9d64c09f53f142dbed5b3cbdd66b8","title":"Sparse Gaussian Processes","order":1},{"id":"seed_6720222c64e5d753921e316c79603c3a","title":"Inducing Points","order":2},{"id":"seed_46cfabd97b873363e7f283b3ac1e2405","title":"Variational Sparse GPs","order":3},{"id":"seed_84407d4684fdbd4381594739e9c9e13f","title":"Random Features","order":4}]},{"id":"seed_f98eb34b8b26e060c9c2ee26e2db04e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 28: Gaussian Processes","sectionOrder":38,"title":"Gaussian Process Classification","order":6,"contentType":"LEARNING","contentSlug":"gp-classification","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7a23fd015cd725a41ea70c93459ada0e","title":"Non-Gaussian Likelihoods","order":0},{"id":"seed_3b23fca59688d6c2375b04e25d34ffa8","title":"Laplace Approximation","order":1},{"id":"seed_55576025f843c85f21cb77f0340088e4","title":"Expectation Propagation for GPC","order":2},{"id":"seed_5583abf2961ce51ff9e44abf45f4bba0","title":"Variational Inference for GP Classification","order":3},{"id":"seed_3fd06af349b053df235e0fbdedd15634","title":"Multi-class Extension","order":4}]},{"id":"seed_848b6363ea4fbdd529dd3bd0015d5968","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Inference Framework","order":1,"contentType":"LEARNING","contentSlug":"variational-inference-framework","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b270dc6d384fec164c3a5ffa57936806","title":"Approximation Motivation","order":0},{"id":"seed_5334dc66eb008957959e9e1aca6201cb","title":"Variational Family","order":1},{"id":"seed_697baf93ff2994b235b0a7012d6ffaa8","title":"KL Divergence Objective","order":2},{"id":"seed_391889b768f9b3aa9a5b2c19e04d47cc","title":"ELBO Derivation","order":3},{"id":"seed_a6a679e3c00facd469f8fc28058d7622","title":"Optimization Perspective","order":4}]},{"id":"seed_347f0f34f42595c07ce415157b687b80","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Mean-Field Approximation","order":2,"contentType":"LEARNING","contentSlug":"mean-field-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_35d87b9d17e7aa2baaa6dd2f76f79061","title":"Factorization Assumption","order":0},{"id":"seed_3caaf10a38f38a2c7243bd4a5cf93751","title":"Coordinate Ascent","order":1},{"id":"seed_e25c385c80d3de57c6f6cbee3625e672","title":"Update Equations","order":2},{"id":"seed_9cf1e718299d4bb42e0164e11f64a045","title":"Convergence","order":3},{"id":"seed_3908eda9710103514494268bd80d7613","title":"Limitations","order":4}]},{"id":"seed_e21603eecdb3d61c60b1ccc6bbea7857","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"ELBO Optimization","order":3,"contentType":"LEARNING","contentSlug":"elbo-optimization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9d035bfefa27b6e6dd43a6ab6180d51b","title":"ELBO Decomposition","order":0},{"id":"seed_100e70a30f3e7feeaabb3b6cce07c042","title":"Reconstruction vs Regularization","order":1},{"id":"seed_ce7e67ef224ee89c4bad7609807fed6e","title":"Gradient Estimation","order":2},{"id":"seed_cf00017793e7e5a60ba7f877b199d54f","title":"Reparameterization Trick","order":3},{"id":"seed_7c3b2d2ffdf44ca6ad0f2d4dfbe5eff1","title":"Score Function Estimator","order":4}]},{"id":"seed_546b35e4882116c3d0a51680f2d835be","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Stochastic Variational Inference","order":4,"contentType":"LEARNING","contentSlug":"stochastic-variational-inference","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fe2a182a076e39308d422e8a4ba94ff7","title":"Mini-Batch Optimization","order":0},{"id":"seed_0d03724f33b7d50b512e2cc5d8ef84dd","title":"Natural Gradients","order":1},{"id":"seed_12ae445dee97b248987053aeb333137f","title":"Scale to Large Datasets","order":2},{"id":"seed_3500ed004a93da01992853d1169686bd","title":"Convergence Analysis","order":3},{"id":"seed_c749740f3ff265cf601ab5aa8b1337fa","title":"Practical Considerations","order":4}]},{"id":"seed_1463afd50f358c4c3ce2e904178881a7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Variational Autoencoders","order":5,"contentType":"LEARNING","contentSlug":"ml-variational-inference-variational-autoencoders","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_02a73b52fc5f2fd4e84443e955de4f9d","title":"VAE Formulation","order":0},{"id":"seed_60c22382d04b7bdfa8e5071abdc4d7ef","title":"Encoder-Decoder Structure","order":1},{"id":"seed_04d8e09c2145402c2fcc906022518db9","title":"Reparameterization","order":2},{"id":"seed_36af5f01177448f5227301364e6a4a54","title":"Training Procedure","order":3},{"id":"seed_b651af28bc7bb8472a36798443e616f3","title":"Latent Representations","order":4}]},{"id":"seed_e9b79bdc20ae45b33f8b2f486ee6c680","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 29: Variational Inference","sectionOrder":39,"title":"Advanced VI Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-vi-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9ef2119aac900ff46fbf91189ec0301e","title":"Normalizing Flows","order":0},{"id":"seed_8f9e86a4e4bbd545f3c0b1947b4c42ca","title":"Amortized Inference","order":1},{"id":"seed_2f4799e1474b7d7db97ebeb03ae60115","title":"Implicit Variational Inference","order":2},{"id":"seed_acad2dd93840c5c83d6252742db67b57","title":"Hierarchical Variational Inference","order":3},{"id":"seed_7497c9c5caf7d99d35c5ec41ed80e4ed","title":"Variational Inference for Deep Learning","order":4}]},{"id":"seed_f82ea691ae02c2164db9a4a902cfee61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Biological Inspiration","order":1,"contentType":"LEARNING","contentSlug":"biological-inspiration","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_09795b81e9e1a2622018f5d8e70f2466","title":"Biological Neurons","order":0},{"id":"seed_e79fa17804429f447bfdfb27eceb0f48","title":"Artificial Neuron Model","order":1},{"id":"seed_e082c6ca8cca833d55b10990aef224b4","title":"Historical Development","order":2},{"id":"seed_1eac52ac8c54d7eaf42bca6a4cf7b1ba","title":"Perceptron Learning Rule","order":3},{"id":"seed_093f74f1ef7e8c78ba3881a14cdf2a7d","title":"XOR Problem","order":4}]},{"id":"seed_763ec03462b28e436c290ff5a6028cdd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Multi-Layer Perceptrons","order":2,"contentType":"LEARNING","contentSlug":"multi-layer-perceptrons","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0cd2f7c86006edf3eb8ac6ca09ba6f8c","title":"Network Architecture","order":0},{"id":"seed_4a63d2dcb5c0ee2f9070e4cc21d7ae46","title":"Hidden Layers","order":1},{"id":"seed_0ffe97f23e7ddcc3198df68d55085b9f","title":"Forward Propagation","order":2},{"id":"seed_fe4c92fae58a44647d55da9f9eb2e003","title":"Matrix Formulation","order":3},{"id":"seed_e3d4d5b90e54ba94bbca9f088829fb4e","title":"Activation Functions","order":4}]},{"id":"seed_a798e45c47f2ce91b6ef5d8994a2778d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Universal Approximation","order":3,"contentType":"LEARNING","contentSlug":"universal-approximation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_85999d1d69cf025cbdfed8400dd52b14","title":"Universal Approximation Theorem","order":0},{"id":"seed_e5209a9aa7c7388a56e283dd309a8f3b","title":"Width vs Depth","order":1},{"id":"seed_865b26f5533a6942c17a0d2d85dea287","title":"Approximation Bounds","order":2},{"id":"seed_47ee926ff0a8002b5267c14d92a61d82","title":"Practical Implications","order":3},{"id":"seed_c4c530c165bec8374e8c11b3dcec8f9d","title":"Limitations","order":4}]},{"id":"seed_06736591bc987a4d2945f9be6f8633ce","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Functions","order":4,"contentType":"LEARNING","contentSlug":"activation-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_994f74896dda5de4b88fa18932dfd8c8","title":"Sigmoid and Tanh","order":0},{"id":"seed_babab3a6409bb5a0bcbf2b7a0499004e","title":"ReLU and Variants","order":1},{"id":"seed_fcbc5dde70e3f25ba4ac7a6168b06968","title":"Swish and GELU","order":2},{"id":"seed_d0013e053d9066c19ca036c1a0bb8450","title":"Softmax","order":3},{"id":"seed_1253ea8c8ea48b0d316c5ac37be8148d","title":"Selection Guidelines","order":4}]},{"id":"seed_ef1c8026b6efc42f60f2b27ef58f6ed5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Loss Functions","order":5,"contentType":"LEARNING","contentSlug":"loss-functions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ad8985203ebcb91702216a5dd51df523","title":"Cross-Entropy Loss: The Foundation of Classification","order":0},{"id":"seed_3b9abab61e06c35b811df0371f135d91","title":"Mean Squared Error: The Regression Workhorse","order":1},{"id":"seed_0ac456a1c487ceaf545ffde76cf4c02b","title":"Hinge Loss: The Max-Margin Perspective","order":2},{"id":"seed_63034c105be2ada50b5aab671c474f22","title":"Focal Loss: Conquering Class Imbalance","order":3},{"id":"seed_70fdc0b14d231418844e68c1758af1f2","title":"Custom Losses: Encoding Domain Knowledge","order":4}]},{"id":"seed_6cc64eb0e0ba1c8951cbe4fed8d38c06","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Output Layers","order":6,"contentType":"LEARNING","contentSlug":"output-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c2bf3107cb745f7d0c5d910e2b6ea99","title":"Regression Outputs","order":0},{"id":"seed_523d8f5cc9b36802d130e4ad94ed6ab1","title":"Binary Classification","order":1},{"id":"seed_810be67dce7c7d9da01411e514e65dca","title":"Multi-Class Classification","order":2},{"id":"seed_d8917b64dcb1b0d083d6f0aaf9e9c1a7","title":"Multi-Label Classification","order":3},{"id":"seed_d1ba82583dbdc5a2157771b0702ee982","title":"Structured Outputs","order":4}]},{"id":"seed_dd77b6b05260cf5c413474e5d189ece4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":7,"contentType":"VISUALIZER","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_0d1e9a5fd7d14cd9c1f49cb2ce5b143b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 30: Neural Network Foundations","sectionOrder":40,"title":"Activation Function Gradient Computation","order":8,"contentType":"PROBLEM","contentSlug":"activation-gradient-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_81b4675ff4c64769f9e3ca3a0e12a3dc","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Backpropagation Algorithm","order":1,"contentType":"LEARNING","contentSlug":"backpropagation-algorithm","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_eff69e34e6a4d9a78415a3b73b7e1196","title":"Chain Rule Application","order":0},{"id":"seed_d959f945d8584814ef099673c86bba90","title":"Gradient Flow","order":1},{"id":"seed_ff74e2467f5c5f7f76cc7ae408bff851","title":"Jacobian-Vector Products","order":2},{"id":"seed_58cf9f9e52f36d66a2df339c4818e299","title":"Memory Considerations","order":3},{"id":"seed_66bd669e4e9fdd0bc2374a7e0a829658","title":"Gradient Checkpointing","order":4}]},{"id":"seed_6f6f054ba88424be3541f892015658c4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Gradient Descent Variants","order":2,"contentType":"LEARNING","contentSlug":"gradient-descent-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_31f04c4f020d5d11bfbd167825753708","title":"Batch Gradient Descent","order":0},{"id":"seed_71eb870ab243ce2db5396ccfd0ec9321","title":"Stochastic Gradient Descent","order":1},{"id":"seed_898b936ec29fe46dfd80f877f1d81783","title":"Mini-batch SGD","order":2},{"id":"seed_4e2a774e5f6bc6a8d37d51f134b3e0d1","title":"Learning Rate Selection","order":3},{"id":"seed_4d15965c24fcbe0048b6818491f8a714","title":"Convergence Analysis","order":4}]},{"id":"seed_0d04bb8be0289593e83fa5d0b2ad1b85","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Adaptive Optimizers","order":3,"contentType":"LEARNING","contentSlug":"adaptive-optimizers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cc76c352785b4f98854d5ba159f82af2","title":"Momentum","order":0},{"id":"seed_b8d533807eca035e291228e136e945be","title":"Nesterov Momentum","order":1},{"id":"seed_ddbe73d641a62006957d2bcabf12a848","title":"AdaGrad","order":2},{"id":"seed_66928aace0a0f6062202dfe580f522fa","title":"RMSprop","order":3},{"id":"seed_efe9e0e3ca3c1773cf07503283ce8375","title":"Adam and Variants","order":4}]},{"id":"seed_790d19157f66aa34b92db31dd4a5173b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation And Optimization","sectionOrder":41,"title":"Learning Rate Schedules: Orchestrating Optimization Dynamics","order":4,"contentType":"LEARNING","contentSlug":"learning-rate-schedules","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5d4c9f518ffedd49c8189e80ceb3efda","title":"Step Decay: The Foundation of Learning Rate Scheduling","order":0},{"id":"seed_43dd95a338d8fe04fad3a07b71cd7e1f","title":"Exponential Decay: Smooth and Continuous Learning Rate Reduction","order":1},{"id":"seed_4646f4dae6efa8af550485a31a0fd7e7","title":"Cosine Annealing: Elegant Schedules for Modern Deep Learning","order":2},{"id":"seed_22d272dfe8d8224f6c895c18b8a9cd62","title":"Warmup Strategies: Stabilizing Early Training Dynamics","order":3},{"id":"seed_b3f0e57b2eadf1fa9f6789beee9be3f0","title":"Cyclical Learning Rates: Oscillating for Better Exploration","order":4}]},{"id":"seed_62fc9f7677c99703f946f7592b4dd208","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Computational Graphs","order":1,"contentType":"LEARNING","contentSlug":"computational-graphs","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_98a0cd5c2fdc648a230c9bb3a45f50ca","title":"Graph Representation","order":0},{"id":"seed_7442668ae46d23e093667429d61cdec4","title":"Forward Pass Computation","order":1},{"id":"seed_62df7feb434cda59e76ccf0b417cfacb","title":"Reverse Mode Autodiff","order":2},{"id":"seed_8f3ea27b8d6099d34c4f50185b683bc5","title":"Topological Ordering","order":3},{"id":"seed_8d00fe7b864b6495d2fd40c47e91a75d","title":"Modern Frameworks","order":4}]},{"id":"seed_b49f7466079a1b6662ac948c9e560dae","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Second-Order Optimization Methods","order":2,"contentType":"LEARNING","contentSlug":"second-order-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_19e82105f99cb13615858bf9816c8969","title":"Newton's Method","order":0},{"id":"seed_6e60b0c142964bea06b9a810a1a23109","title":"Hessian-Free Optimization","order":1},{"id":"seed_ce0869517b6da49b3351c6b997085aa2","title":"Natural Gradient Descent","order":2},{"id":"seed_fd3dedebe037eae5b6f0845cc897ebb7","title":"K-FAC: Kronecker-Factored Approximate Curvature","order":3},{"id":"seed_3bf865167da3f29ca475e5725a9e1858","title":"Practical Limitations of Second-Order Methods","order":4}]},{"id":"seed_c856bc9ccfc814ca27e37c25317189eb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":3,"contentType":"VISUALIZER","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_5e38cad8f738a9edc45cd7bb25048d92","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Training a Single Neuron with Gradient Descent","order":4,"contentType":"PROBLEM","contentSlug":"single-neuron-backpropagation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_79363c50d7cc2df59cf264620abf9d14","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 31: Backpropagation Optimization","sectionOrder":42,"title":"Gradient Computation Engine (Basic Autograd)","order":5,"contentType":"PROBLEM","contentSlug":"gradient-computation-engine","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_1a37ecaa3606e5645687fde6f75686e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Capacity and Generalization","order":1,"contentType":"LEARNING","contentSlug":"capacity-and-generalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e831a2d571a02aed7b2594ebaaa04e99","title":"Model Capacity","order":0},{"id":"seed_33126647065e44994247dc961a5df5a9","title":"Effective Capacity","order":1},{"id":"seed_596efbdc5f4522ccc9f2b6cfcd3800da","title":"Double Descent","order":2},{"id":"seed_cf29ecc66b207003fd45ec5afd3fdf84","title":"Overparameterization","order":3},{"id":"seed_cba4da4251a70bba57f47ee09aaa1651","title":"Implicit Regularization","order":4}]},{"id":"seed_a4556ac961fca197a64db759fdfd37ff","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Weight Regularization","order":2,"contentType":"LEARNING","contentSlug":"weight-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_66cb62633e1dbeb921aa0beafbd0ddb9","title":"L2 Weight Decay","order":0},{"id":"seed_c6a37536dfbdd1d841d3eb06a3e8a3aa","title":"L1 Sparsity","order":1},{"id":"seed_14441f0dee1ac0d88cbc6e7ba7a0c8ee","title":"Weight Decay vs L2 Regularization","order":2},{"id":"seed_9adb308bfc2fd6ec34705d7dea8fdd8e","title":"Max-Norm Constraints","order":3},{"id":"seed_a062e3d2eb16f2b58cd538b077020618","title":"Spectral Normalization","order":4}]},{"id":"seed_28b239ae9dfb30b8bddecfbaa3928c28","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Dropout","order":3,"contentType":"LEARNING","contentSlug":"dropout","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_76e9d37c919e8e1fb632722025c61433","title":"Dropout Training","order":0},{"id":"seed_c5955f328d7cd9c3282aa35c5e14782b","title":"Inverted Dropout","order":1},{"id":"seed_ab0693d1e403a509572c9cd80c8732c3","title":"Dropout as Bayesian Inference","order":2},{"id":"seed_ede03534f09a000b408afe81a7343fcd","title":"Variational Dropout","order":3},{"id":"seed_41f81c48f9d5b04ed469d5b03be68d3a","title":"Dropping Other Things","order":4}]},{"id":"seed_e44f2f1dee2954db26113a899c23bb10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Batch Normalization","order":4,"contentType":"LEARNING","contentSlug":"batch-normalization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0f8986233dc6e652d6eca1dcd5b6ec52","title":"Internal Covariate Shift","order":0},{"id":"seed_7c92b0ea3332c4e8d76f7a2232bf295c","title":"BatchNorm Formulation","order":1},{"id":"seed_4f6967b178b0b83fe6b8b09d9cece69d","title":"Training vs Inference","order":2},{"id":"seed_6a24beffebc8a6a833a86d42d5c48e17","title":"Layer Normalization","order":3},{"id":"seed_8df6bd4733f3d741b8f810f93f181077","title":"Other Normalizations","order":4}]},{"id":"seed_09158c8adb4d1dbd43a885617bdbd7b1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Data Augmentation: Expanding Training Distributions","order":5,"contentType":"LEARNING","contentSlug":"data-augmentation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9ddecc4088469f1893d563abcbf646be","title":"Image Augmentations: Foundations and Techniques","order":0},{"id":"seed_7c1373a71f4635b1af840b6276322a4c","title":"Mixup and CutMix: Mixing-Based Data Augmentation","order":1},{"id":"seed_bc0beb3b967020a151e29fe32a862951","title":"AutoAugment: Learning Augmentation Policies","order":2},{"id":"seed_178c0b732dabc8ea880a6a57ec65d5c7","title":"Test-Time Augmentation: Inference-Time Enhancement","order":3},{"id":"seed_52b6ac25cb1fe968f5a96985d44f1c92","title":"Augmentation Strategies: Synthesis and Best Practices","order":4}]},{"id":"seed_020e4e8a002e13bfe417d1aac7d02803","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 32: Regularization In Deep Learning","sectionOrder":43,"title":"Implicit Regularization in Deep Learning","order":6,"contentType":"LEARNING","contentSlug":"implicit-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7ca88cad7675aa38243888ce35d52260","title":"SGD Implicit Bias","order":0},{"id":"seed_8a1707d36f31f6f2d2ed0a42e6afd9b6","title":"Architecture Choices as Implicit Regularization","order":1},{"id":"seed_540020b3f5a34922dc0e9ad8d984dfb3","title":"Early Stopping as Implicit Regularization","order":2},{"id":"seed_671bc5b4ce775b82a426490db3a2f30c","title":"The Lottery Ticket Hypothesis","order":3},{"id":"seed_8180037be2d488f14c415351d0d9a86e","title":"Benign Overfitting","order":4}]},{"id":"seed_6851d7814e8e4b64902dfba5a609d2de","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"The Convolution Operation","order":1,"contentType":"LEARNING","contentSlug":"convolution-operation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_bc5a5d3af4d576d5c5c39ac8cb35dcb3","title":"Discrete Convolution","order":0},{"id":"seed_b6eff19114cf6f6d52b3be16461b5ab8","title":"Cross-Correlation","order":1},{"id":"seed_f46a8e0250ea27f646f358b4ae454f0b","title":"Stride and Padding","order":2},{"id":"seed_d77029408295323d9361e684f4fe1806","title":"Dilation (Dilated/Atrous Convolutions)","order":3},{"id":"seed_d05d3969c9c32935845800f66f3a4e4c","title":"Implementation Details","order":4}]},{"id":"seed_94702d5823572341bdb7c6dac7a81e0d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Convolutional Layers","order":2,"contentType":"LEARNING","contentSlug":"convolutional-layers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_67f3b0d3bae43cf5233572b3987dfc42","title":"Parameter Sharing","order":0},{"id":"seed_03dab9165c54883bf956ff0be23415ef","title":"Translation Equivariance","order":1},{"id":"seed_b09f7fe0c91be0ae4c185878e55488de","title":"Receptive Field","order":2},{"id":"seed_c33c48b5de68ec688a993992134dafc1","title":"Feature Maps","order":3},{"id":"seed_8df1464c949a56d95c7f98233a4b3a85","title":"Multiple Channels","order":4}]},{"id":"seed_4af0cc0135bdb4df0247addfc29fe2ee","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Pooling and Downsampling","order":3,"contentType":"LEARNING","contentSlug":"pooling-and-downsampling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_57f00e01aec45e3c71871485d6c8dcae","title":"Max Pooling","order":0},{"id":"seed_133eb9365db393949e0e4c7a58533ce5","title":"Average Pooling","order":1},{"id":"seed_97de879f0cbfb947126e698bd6a407a5","title":"Global Pooling","order":2},{"id":"seed_d93b1cff66a91a40266d0acc1a8e304b","title":"Strided Convolutions","order":3},{"id":"seed_b4181af4b65d6c7a25fe248ffbb8b4c6","title":"Pooling Alternatives","order":4}]},{"id":"seed_cd8e784eebe78b202a0a8c12e492d917","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"CNN Architectures: From LeNet to Modern Designs","order":4,"contentType":"LEARNING","contentSlug":"cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_da006f47f7b2e63e0094b8bcd50dc116","title":"LeNet: The Pioneer of Convolutional Neural Networks","order":0},{"id":"seed_15ac2d1e831e44637cbd4ddf2582d341","title":"AlexNet: The Deep Learning Revolution","order":1},{"id":"seed_b8c8db8c8f30842a71af5fe95722c186","title":"VGGNet: The Power of Depth and Simplicity","order":2},{"id":"seed_a85e83688612604dd5a028eb51a4d27b","title":"Inception/GoogLeNet: Going Deeper with Efficiency","order":3},{"id":"seed_fb4050bdef31b6610c595dd031364946","title":"CNN Design Principles: From Theory to Practice","order":4}]},{"id":"seed_3437e70f0dc0731e390eaacb69b9232e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Residual Networks and Skip Connections","order":5,"contentType":"LEARNING","contentSlug":"residual-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a64204b14a8b39b72ffcaf5b2588dbb6","title":"Skip Connections: Enabling Deep Network Training","order":0},{"id":"seed_c7765d84be586ac599eebd2915d3e754","title":"ResNet Architecture: Building Very Deep Networks","order":1},{"id":"seed_f3fff98dedd75800f6c8d58816462110","title":"Identity Mappings: Optimizing Information Flow","order":2},{"id":"seed_846e0425bf740ccce676ff8451997e2b","title":"DenseNet: Maximizing Feature Reuse","order":3},{"id":"seed_f5e566f040297bb7614357df26320063","title":"ResNeXt: Aggregated Residual Transformations","order":4}]},{"id":"seed_308ce9bd101b8dd160bbae4d0fb7454d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"Modern CNN Architectures","order":6,"contentType":"LEARNING","contentSlug":"modern-cnn-architectures","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_6bb2b4bae93bbab9d03778f529b51523","title":"EfficientNet: Compound Scaling for Superior CNNs","order":0},{"id":"seed_1113f9c96c13516295f3abbfb48378ac","title":"MobileNet & ShuffleNet: Efficient Architectures for Edge Deployment","order":1},{"id":"seed_05de84b8d8fc43d6c09c2767b3e187da","title":"Neural Architecture Search: Automating Network Design","order":2},{"id":"seed_2bb4ecc047b309d59196f1e63f305543","title":"ConvNeXt: A ConvNet for the 2020s","order":3},{"id":"seed_a8b738aa653191747d48efd117acd8c5","title":"Vision Transformers Connection: CNNs Meet Attention","order":4}]},{"id":"seed_cbee09a0e607620cd98f7ddd2e9fa3d1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":7,"contentType":"VISUALIZER","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_110330320fbff6e7329228ba2e790938","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 33: Convolutional Neural Networks","sectionOrder":44,"title":"2D Convolution Operation","order":8,"contentType":"PROBLEM","contentSlug":"convolution-2d-operation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_5f1f573d9929ca2f3046a5cc8bcc8aca","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Sequence Modeling","order":1,"contentType":"LEARNING","contentSlug":"sequence-modeling","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_235183e88e4f00a358e8d13db4003a6c","title":"Sequential Data Types","order":0},{"id":"seed_df45c797e69eac247606b74705241657","title":"Modeling Dependencies","order":1},{"id":"seed_be0d2845a5d96a8c226095478a442881","title":"Autoregressive Models","order":2},{"id":"seed_9ae638bfc09e87c05e1da688aa26f00c","title":"RNN Motivation","order":3},{"id":"seed_2e81eaf6aab701e54de2dd278acf8839","title":"Unfolded Computation","order":4}]},{"id":"seed_5db2b8211deeb14b772472b6b9cf59a0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Recurrent Neural Network Architecture","order":2,"contentType":"LEARNING","contentSlug":"rnn-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ffb4c14fff3dfbc033bd73f8472d88b","title":"Hidden State Dynamics","order":0},{"id":"seed_70f8fd4429bef62d71f966e83651d04f","title":"Parameter Sharing in Time","order":1},{"id":"seed_c8e71e12f1e158389e95fedd69b73949","title":"Forward Computation","order":2},{"id":"seed_b6ea07778fb37bcf3351d71ea728f91e","title":"Backpropagation Through Time","order":3},{"id":"seed_5c16d9657942ccab97b5ff1e49bf5a96","title":"Computational Considerations","order":4}]},{"id":"seed_162d876b2b2a4f4479fbf1d37a5e4a64","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Vanishing and Exploding Gradients","order":3,"contentType":"LEARNING","contentSlug":"vanishing-exploding-gradients","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_91a92d15458eda0c1c88fdecbbc485de","title":"Gradient Flow Analysis","order":0},{"id":"seed_1ae50a5a3081a2de8f5f35f809d81393","title":"The Vanishing Gradient Problem","order":1},{"id":"seed_50a867c556aa8b4814cb6caad5c791f2","title":"The Exploding Gradient Problem","order":2},{"id":"seed_b400e5dcb221744be78b18270112d079","title":"Gradient Clipping","order":3},{"id":"seed_0c2da4274f085a450a4df0e1aee3117f","title":"Better Architectures","order":4}]},{"id":"seed_3820671dcdd4232fc4cdd4d868cea6d2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Long Short-Term Memory","order":4,"contentType":"LEARNING","contentSlug":"long-short-term-memory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_fb984fd798419db31c63f8a5cecbbbb8","title":"LSTM Cell Architecture","order":0},{"id":"seed_7ecb28a76098a5c89ff6e72cf3e7d570","title":"Gate Mechanisms","order":1},{"id":"seed_328316103f8585f2c20c6742f237e579","title":"Cell State Highway","order":2},{"id":"seed_fce6c698887b2b2d151159db90ed96a5","title":"Gradient Flow Improvement","order":3},{"id":"seed_c5e35e5a2ea9d486eb547193536f2ece","title":"LSTM Variants","order":4}]},{"id":"seed_fa7c5556299f5f97c15a403a7b11cb8c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Gated Recurrent Units","order":5,"contentType":"LEARNING","contentSlug":"gated-recurrent-units","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e4c40359099fa0097e10411d1bd0efa7","title":"GRU Simplification","order":0},{"id":"seed_21b2b127f5b79920ff2684572b60a73c","title":"Update and Reset Gates","order":1},{"id":"seed_1dbe208580244da5aecff1214c7dcfc4","title":"GRU vs LSTM","order":2},{"id":"seed_7b581d024af0838f906d528de70243a0","title":"Empirical Comparison","order":3},{"id":"seed_ccaa48691d6129006a9ff25747a9e59b","title":"When to Use Which","order":4}]},{"id":"seed_37f61dfc75c977d4f49c5d259f50879f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"Advanced RNN Topics","order":6,"contentType":"LEARNING","contentSlug":"advanced-rnn-topics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_409b2dcb729d9cace5f00fdde20d22ba","title":"Bidirectional RNNs","order":0},{"id":"seed_3b30e362f910c52ac1e0e6992fe8153e","title":"Deep RNNs","order":1},{"id":"seed_c8248c383b61031977ab62a8dcbc7394","title":"Sequence-to-Sequence","order":2},{"id":"seed_830013df4edc054f523accca501c138c","title":"Encoder-Decoder","order":3},{"id":"seed_c3d21849ccc390fd5ca72f4f4380ac58","title":"Attention Preview","order":4}]},{"id":"seed_78f2403d64ba60eab0edef165e79785d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":7,"contentType":"VISUALIZER","contentSlug":"lstm-memory-network","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_e56a4e18774f4156d0a77d0180048778","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 34: Recurrent Neural Networks","sectionOrder":45,"title":"LSTM Memory Network: Gated Recurrent Memory Cell","order":8,"contentType":"PROBLEM","contentSlug":"lstm-memory-network","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6a5518e35a9be244d03d87d3d56f50e8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Attention Mechanism","order":1,"contentType":"LEARNING","contentSlug":"attention-mechanism","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b14bf2d6fac6f8043c7a3ceccebb10ee","title":"Attention Intuition","order":0},{"id":"seed_b8198f9c2f13b55af2d6cc72c70da1fd","title":"Query-Key-Value Framework","order":1},{"id":"seed_bfb6f6741e37512a120b3c81aabff1e7","title":"Soft Attention","order":2},{"id":"seed_ee5ab2b20568fb8d3bf7f324302fd0fb","title":"Hard Attention","order":3},{"id":"seed_45e607b88ddea1964fe1ff474f6db67e","title":"Attention Visualization","order":4}]},{"id":"seed_e9ba71f8e6d0d3c6af728ccfb81965d5","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention","order":2,"contentType":"LEARNING","contentSlug":"self-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_51168ac1cf7f9f28ef6fad645e00e895","title":"Self-Attention Formulation","order":0},{"id":"seed_7bfaa9e65ec2d6d85ac929e7ad8c3254","title":"Computing Attention Weights","order":1},{"id":"seed_8b3c42e8f86b6600e5a13fe1a2d91bff","title":"Scaled Dot-Product Attention","order":2},{"id":"seed_0a3588b63f585e220f05b29da89fdd50","title":"Position Independence","order":3},{"id":"seed_34d29cb9d119c91773c517a8f7fa4ed7","title":"Complexity Analysis","order":4}]},{"id":"seed_2e1937ca8c0da75933b343ae37dc2cd2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Multi-Head Attention","order":3,"contentType":"LEARNING","contentSlug":"multi-head-attention","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c1e2d8137df1cedb4585287f81565d1","title":"Multiple Attention Heads","order":0},{"id":"seed_3d322f9a99be5576157c6859a91876e0","title":"Head Concatenation","order":1},{"id":"seed_0b7fdd66835d7aedb0877a1cd7cb8a6e","title":"Parallel Computation","order":2},{"id":"seed_6d9c34e0301b0e62d06abbcc64449582","title":"What Heads Learn","order":3},{"id":"seed_73dd50a18ebfe29f19b1ff4c071b2ebb","title":"Head Pruning","order":4}]},{"id":"seed_042e91151586cd6a2bb93b767eee318d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Architecture","order":4,"contentType":"LEARNING","contentSlug":"transformer-architecture","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c01e6c661546f21ccdc58bdf4005717b","title":"Encoder-Decoder Structure","order":0},{"id":"seed_5e626cab9eeed77508a08fc546c5ca91","title":"Layer Normalization","order":1},{"id":"seed_703f97d66ba79df132f49ae96d5f63f6","title":"Feed-Forward Layers","order":2},{"id":"seed_de0cd90d14bfb5700dbc1bb980e91444","title":"Residual Connections","order":3},{"id":"seed_db03a67fd0c8d2dbfa0037454a49e568","title":"Full Architecture","order":4}]},{"id":"seed_79661700b7c47fae120d0bd30aa077f8","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Transformer Variants","order":5,"contentType":"LEARNING","contentSlug":"transformer-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2c4d740414ff0c3525de6d1c3ac4f66f","title":"BERT: Bidirectional Encoder Representations from Transformers","order":0},{"id":"seed_246305c23c09675a6c8b4e49d98cec2b","title":"GPT: Generative Pre-trained Transformer","order":1},{"id":"seed_ae0672cd732ecc5cff8ede5bb4829e7d","title":"T5: Text-to-Text Transfer Transformer","order":2},{"id":"seed_7c44fa4d4d359f2bb346c2f3eeebd71f","title":"Efficient Transformers: Overcoming the Quadratic Bottleneck","order":3},{"id":"seed_9bbffc5fedc1fc914aa2a8cc10f17fc3","title":"Vision Transformers: Transformers Meet Computer Vision","order":4}]},{"id":"seed_2c16c77d2922828776983c6932e17364","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":6,"contentType":"VISUALIZER","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":15,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6ea15e630262800d691de2dbdb9277ad","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention And Transformers","sectionOrder":46,"title":"Self-Attention Weighted Aggregation","order":7,"contentType":"PROBLEM","contentSlug":"self-attention-weighted-aggregation","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_ea4826b8cb5aa5c6c91df6bbd5b3562f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 35: Attention Transformers","sectionOrder":47,"title":"Positional Encoding","order":1,"contentType":"LEARNING","contentSlug":"positional-encoding","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_21382e3099cbb582b52b8a1184c2e406","title":"Position Importance","order":0},{"id":"seed_0c95bea8a5315a2b15d24440278caad9","title":"Sinusoidal Encoding","order":1},{"id":"seed_58390aa6bf0ebac42bb5856dc84e1c13","title":"Learned Positional Embeddings","order":2},{"id":"seed_ea0113872b05c0dd5eebd4513a402d38","title":"Relative Positional Representations","order":3},{"id":"seed_da4b02358ea0119f86c27c10168d708e","title":"Rotary Position Embeddings (RoPE)","order":4}]},{"id":"seed_8a58a3ebe0e519a060e593bb2f787e77","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Model Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"generative-model-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_49b537de6e9908485eaad849bb0bcf7e","title":"Generative vs Discriminative","order":0},{"id":"seed_408ad22b4a2a0bf8eb924405fe68f7c8","title":"Density Estimation","order":1},{"id":"seed_65afd7be1fa1b93b9ba03547bc72070f","title":"Sampling","order":2},{"id":"seed_9cb97bc76756a5a731ce25ed5823c230","title":"Latent Variables","order":3},{"id":"seed_bc25a1164cc3bc0ecd0912bbf1222d28","title":"Evaluation Challenges","order":4}]},{"id":"seed_04aa20659bf19165d9aef4e2b23a6ff7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Generative Adversarial Networks","order":2,"contentType":"LEARNING","contentSlug":"generative-adversarial-networks","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_92cde40b3bdeccae82ecf4550a808c4e","title":"GAN Framework","order":0},{"id":"seed_acfa8120ad108063d20858f36f62a7fc","title":"Generator and Discriminator","order":1},{"id":"seed_d553a28de7ee150938984a8729e2bb57","title":"Minimax Objective","order":2},{"id":"seed_a389135607c562d3b5d855582661f7ae","title":"Training Dynamics","order":3},{"id":"seed_f3b99f7f9d04f1679b474545aa941b7c","title":"Mode Collapse","order":4}]},{"id":"seed_fbcfefe6c406993e3296659d69308542","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"GAN Variants","order":3,"contentType":"LEARNING","contentSlug":"gan-variants","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_11d9ac658b53d6c176c460daeea9d74e","title":"DCGAN: Deep Convolutional Generative Adversarial Networks","order":0},{"id":"seed_769d897f8f96479073ff98a359650cf0","title":"Wasserstein GAN: A New Paradigm in GAN Training","order":1},{"id":"seed_ff0cf933079c08ece6b6faf036e0c068","title":"Progressive GAN: Growing GANs From Low to High Resolution","order":2},{"id":"seed_5e24cbada0120706452c4825fcc79bbf","title":"StyleGAN: Style-Based Generation with Disentangled Latent Spaces","order":3},{"id":"seed_3641ef9444486545ccc912a0c81da198","title":"Conditional GANs: Controlled Generation Through Conditioning","order":4}]},{"id":"seed_b7c820446f87f68f23f8ec05b9981baf","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Flow-Based Models","order":4,"contentType":"LEARNING","contentSlug":"flow-based-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_84905931e293f2b831882abf16d6df8f","title":"Normalizing Flows: Foundations and Principles","order":0},{"id":"seed_0cb2069db39199813bba39c7f9c750cb","title":"Change of Variables: The Mathematical Heart of Flows","order":1},{"id":"seed_ffeba17620712c8ab9ba1c17ed0aefd6","title":"Coupling Layers: The Architectural Breakthrough","order":2},{"id":"seed_a49ca4463ab83e030fce5eb9bbd9b015","title":"RealNVP and Glow: State-of-the-Art Image Flows","order":3},{"id":"seed_b306fff82bd6bbca0f5f48590a4700fd","title":"Continuous Normalizing Flows: Neural ODEs","order":4}]},{"id":"seed_ec6ae3647c56081215e3cbf9ded2e8dd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 36: Generative Models","sectionOrder":48,"title":"Diffusion Models","order":5,"contentType":"LEARNING","contentSlug":"diffusion-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7acdba52274ee2acd853cc957f07dc38","title":"The Diffusion Process","order":0},{"id":"seed_212a0dbff143bd8449367dfce583c578","title":"Denoising Score Matching","order":1},{"id":"seed_1a240b17a7991aa7338d80747ee8e174","title":"Denoising Diffusion Probabilistic Models (DDPM)","order":2},{"id":"seed_a1d1876bafaa3bd54199fb1f1e308078","title":"Score-Based Generative Models","order":3},{"id":"seed_b02b79f05d5bc6fcd395a71923c782c6","title":"State of the Art in Diffusion Models","order":4}]},{"id":"seed_904927660fd8d6c41d1aba5f40bf3672","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised And Self Supervised Learning","sectionOrder":49,"title":"The Label Scarcity Problem","order":1,"contentType":"LEARNING","contentSlug":"the-label-scarcity-problem","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b94d1c67041286d072d179c5c6680f87","title":"Labeling Costs: The Economics of Data Annotation","order":0},{"id":"seed_59ce4cb0d7c7fdd66dbd495cebeb3586","title":"The Semi-Supervised Setting: Formal Framework","order":1},{"id":"seed_be3aa6079e6b2923cf296d7b7f7da4dd","title":"Transductive vs Inductive: Two Philosophies of Learning","order":2},{"id":"seed_89de03bd0266972e1cf8907b70e0e46b","title":"Assumptions: The Foundations of Semi-Supervised Learning","order":3},{"id":"seed_e60fec48c39ee91d8a9efcdff1c87f0a","title":"Evaluation Challenges: Measuring SSL Effectiveness","order":4}]},{"id":"seed_e4208222d89c41fea5968e9e5f3c53d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Semi-Supervised Methods","order":1,"contentType":"LEARNING","contentSlug":"semi-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7273b271be996b7f47f56f5a2a8009be","title":"Self-Training","order":0},{"id":"seed_9492bec0dac87a9b35e7dbef9e06b787","title":"Co-Training","order":1},{"id":"seed_6096f8363aa5b263799902c3e60e4a4f","title":"Label Propagation","order":2},{"id":"seed_02f188120173e71fa394a1d94886d485","title":"Graph-Based Methods","order":3},{"id":"seed_3a665d07f5580d4735619d1924070d9a","title":"Generative Approaches","order":4}]},{"id":"seed_3bff5f8dfe463704b7b4b20e9fa5d91c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Consistency Regularization","order":2,"contentType":"LEARNING","contentSlug":"consistency-regularization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3de7771529a2843792cfc6145fa48711","title":"Consistency Assumption","order":0},{"id":"seed_752ca380224c5c691981e62ce2f704d4","title":"Data Augmentation Consistency","order":1},{"id":"seed_cb7701ec811e773e80576da4fa007e92","title":"UDA and FixMatch","order":2},{"id":"seed_80b17f6390eea8a31dda4eb34b2e6f5b","title":"MixMatch","order":3},{"id":"seed_e4943630cb32e04202c86da79bb64ad1","title":"Pseudo-labeling","order":4}]},{"id":"seed_cef5c1d33e1344056ba5a145591e865b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Self-Supervised Learning","order":3,"contentType":"LEARNING","contentSlug":"self-supervised-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_2883594f2d194893b21116db5b10b611","title":"Pretext Tasks","order":0},{"id":"seed_c7429f61bf2faf51351681a835989714","title":"Learning Representations","order":1},{"id":"seed_82c9830dab7e86824ad66c59829aebaa","title":"Contrastive Learning","order":2},{"id":"seed_d7c981901ff8898ddac0987cd67e47da","title":"Non-Contrastive Methods","order":3},{"id":"seed_02264a069440669db434d7a381b56000","title":"Evaluation Protocols","order":4}]},{"id":"seed_28431ac2324f528d7232189a00afdae7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Contrastive Learning","order":4,"contentType":"LEARNING","contentSlug":"contrastive-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_805c0204c5ece435791de4ad03a6bfcf","title":"InfoNCE Loss","order":0},{"id":"seed_e3f13263c9c801fdb5a880285402b803","title":"SimCLR: A Simple Framework for Contrastive Learning","order":1},{"id":"seed_600178cb87e607adafdc78ee3ec01010","title":"MoCo: Momentum Contrast for Unsupervised Representation Learning","order":2},{"id":"seed_34be5e97413d532e59d0c1cc1a3c99fe","title":"Positive and Negative Pairs: The Foundation of Contrastive Learning","order":3},{"id":"seed_fe55c5b1ec3ad1990fdb4b64a5b0e900","title":"Data Augmentation: The Cornerstone of Contrastive Learning","order":4}]},{"id":"seed_89bfe981eb096f5677324a5cb95c5c3f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 37: Semi Supervised Self Supervised Learning","sectionOrder":50,"title":"Modern Self-Supervised Methods","order":5,"contentType":"LEARNING","contentSlug":"modern-self-supervised-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_0668b2b0ab3ceaeafdc7fb1e75d80b4b","title":"BYOL and SimSiam: Learning Without Negative Samples","order":0},{"id":"seed_eedc9472f765deb20e291e6c57703484","title":"Clustering-based Self-Supervised Learning","order":1},{"id":"seed_ae7996966a89fd0ac56c5c45aaa8bf6b","title":"Masked Modeling: Learning by Reconstruction","order":2},{"id":"seed_87b25599782952bbe49daa3aa37eb5af","title":"Vision-Language Pre-training: Bridging Modalities","order":3},{"id":"seed_72dda2305547175d966282b77f3dc5a6","title":"Foundation Model Training: Scaling Self-Supervision","order":4}]},{"id":"seed_48b71cced9af84fce00dd5f87802a040","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning And Domain Adaptation","sectionOrder":51,"title":"Multi-Task Learning","order":1,"contentType":"LEARNING","contentSlug":"multi-task-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ffcb8c9c63efc53b2da00340790dd0e4","title":"Shared Representations","order":0},{"id":"seed_60ca375242ca15c8967bd77412d42ea5","title":"Hard vs Soft Parameter Sharing","order":1},{"id":"seed_beb212c257539a3a79dd7b96fe6a57c4","title":"Task Relationships","order":2},{"id":"seed_2c296bf58f61996727d0f0125ecdafd2","title":"Optimization Challenges","order":3},{"id":"seed_4bbd04a744d4c14f400e7dbe966a3df8","title":"When MTL Helps","order":4}]},{"id":"seed_bad0d98a933a15ea25cabed00cac2414","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Transfer Learning Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"transfer-learning-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4c49302f1e96deff0142c99ba9b7fdc1","title":"Transfer Learning Definition","order":0},{"id":"seed_6281b069bdc73cdb2ba68151fc3cb65a","title":"Source and Target Domains","order":1},{"id":"seed_dbd161b285a64535a5650647505be45a","title":"When Transfer Helps","order":2},{"id":"seed_fb89cf127cd6ac11ad5e5208d4452897","title":"Negative Transfer","order":3},{"id":"seed_ba30e3f2f613d3b1ac9823722506a7eb","title":"Transfer Learning Taxonomy","order":4}]},{"id":"seed_18d69040c7cd9a295f5552d746a8012b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Feature-Based Transfer","order":2,"contentType":"LEARNING","contentSlug":"feature-based-transfer","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3c4720f0f0ef403744cfed039ebb2db0","title":"Pre-trained Representations","order":0},{"id":"seed_b9059313044a9abf54782ad529f442aa","title":"Frozen Features","order":1},{"id":"seed_2115de34d28d39763ec65fc14549a400","title":"Feature Extraction","order":2},{"id":"seed_47f72706dd3b6337fdbe778f234c72a2","title":"Domain Discrepancy","order":3},{"id":"seed_d02d0b44f60101f10247db2ea5b1fa98","title":"Feature Adaptation","order":4}]},{"id":"seed_8e9f01590c85745935d1f7e223be6927","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Fine-Tuning Strategies","order":3,"contentType":"LEARNING","contentSlug":"fine-tuning-strategies","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cb2ca8b4e06104ef5ec44d8b9d921f09","title":"Full Fine-Tuning","order":0},{"id":"seed_c9e4af4829a2d491ea8ad369c71be1df","title":"Selective Fine-Tuning","order":1},{"id":"seed_f203cc52c5eac01973d72ea7a8335c95","title":"Learning Rate Strategies","order":2},{"id":"seed_7717bb86c9fcf13b86cb335ed5d776aa","title":"Catastrophic Forgetting","order":3},{"id":"seed_14fde34458578244ae6d9c6f1e70d993","title":"Regularization Approaches","order":4}]},{"id":"seed_4c02fca3ba18a5932d556e4a7780eeeb","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Domain Adaptation","order":4,"contentType":"LEARNING","contentSlug":"domain-adaptation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_dec6237b8f7cf4ea1ed097071fa796ed","title":"Domain Shift Problem","order":0},{"id":"seed_bdaf0e1a6513a4cd10801361c0f91add","title":"Covariate Shift","order":1},{"id":"seed_8cb79ad18b259180186dba4092c6982a","title":"Distribution Matching","order":2},{"id":"seed_d267b53ccaae39cfbfa6ee1dd98a10f9","title":"Adversarial Domain Adaptation","order":3},{"id":"seed_8dc66df94d35b46b5c94bf32791d5295","title":"Domain Generalization","order":4}]},{"id":"seed_3d3815ce091181ea37c5c2491dc123c9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 38: Transfer Learning Domain Adaptation","sectionOrder":52,"title":"Meta-Learning: Learning to Learn","order":5,"contentType":"LEARNING","contentSlug":"meta-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4ddf3a062957b6b92f302f99a926bd13","title":"Learn to Learn: The Meta-Learning Paradigm","order":0},{"id":"seed_aae4cc1587fe89b803a9e09d34449c5a","title":"Few-Shot Learning: Mastering Minimal Data","order":1},{"id":"seed_75407bcceb85790bfe6832dd1e8eb1a6","title":"Model-Agnostic Meta-Learning (MAML)","order":2},{"id":"seed_9d8f05b14949c177bc456211ddacfad2","title":"Prototypical Networks: Learning to Compare","order":3},{"id":"seed_3a326b65502383487e2ff533c888b48f","title":"Meta-Learning Applications: From Vision to Language to Robotics","order":4}]},{"id":"seed_146bb4046d37075972f5f1d4a35d8133","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"rl-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a1440050fc1a0eb133c023553c92cd0c","title":"Agent-Environment Interaction","order":0},{"id":"seed_da02dc3ca4fe119dc66e4c9b2e0482d9","title":"States, Actions, Rewards","order":1},{"id":"seed_d63fae8c38ef49a11ac345ecf4e39919","title":"Policies","order":2},{"id":"seed_d8a1367304379167e8f13f653e6df947","title":"Value Functions","order":3},{"id":"seed_93008a2220640a580ee8a0c4f3371e0c","title":"Markov Property","order":4}]},{"id":"seed_4a14d44ad08d752faaf01a1fe34edd4b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Markov Decision Processes","order":2,"contentType":"LEARNING","contentSlug":"markov-decision-processes","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_baad5061f468865e0971a0ec531b56fc","title":"MDP Formulation","order":0},{"id":"seed_105b4da2a7df57cb514aaecd9d49f025","title":"State Transitions","order":1},{"id":"seed_45cdf65b967fd294b81f007835337ee3","title":"Reward Signals","order":2},{"id":"seed_04d8628cfd9f5cfa7043d96e60d0572f","title":"Discount Factor","order":3},{"id":"seed_f5dc224b84809ea9eadbede9875a855e","title":"Bellman Equations","order":4}]},{"id":"seed_e962e18f072550c1bddf2875fc11d4d0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Value-Based Methods","order":3,"contentType":"LEARNING","contentSlug":"value-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a44800bd57e063c3c12bd7e79070846a","title":"Value Iteration","order":0},{"id":"seed_685fc314c322a031b19ad648f93d3075","title":"Q-Learning","order":1},{"id":"seed_4d933693440da6c2e6f3330f54017806","title":"SARSA","order":2},{"id":"seed_424dd68e1f950f15aa7d6c32f26c8d11","title":"Temporal Difference Learning","order":3},{"id":"seed_c6be6c35bde2b317180927321e534a83","title":"Eligibility Traces","order":4}]},{"id":"seed_8dafdc1a94769c8c45e42c0a06ed363b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Policy-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"policy-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_01ced883ed583aaa3a6c3ff114050637","title":"Policy Gradient Theorem","order":0},{"id":"seed_321db62cc5d4859530196072c241f8d3","title":"REINFORCE Algorithm","order":1},{"id":"seed_97ca06768ff43841cab3a97d2c0010e2","title":"Variance Reduction","order":2},{"id":"seed_2d727389d38bbaa56cdf4795491da792","title":"Actor-Critic Methods","order":3},{"id":"seed_e0a16febcec9ed7a7011fc3c799e57b9","title":"Advantage Functions","order":4}]},{"id":"seed_306e6c5664e5b5241b8ca0658b77ed61","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Deep Reinforcement Learning","order":5,"contentType":"LEARNING","contentSlug":"deep-reinforcement-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_85dabf15f96af8b65718437828eaef57","title":"DQN Architecture","order":0},{"id":"seed_0e902fe19447c2a8731f33a83425dded","title":"Experience Replay","order":1},{"id":"seed_bdf4a231c531d0aae0a56d3fcdd19a81","title":"Target Networks","order":2},{"id":"seed_94f6aeeff0d2b84b2dad53a4a763133d","title":"Rainbow DQN","order":3},{"id":"seed_286d78957c58bfa73e325fa572f98d27","title":"PPO and SAC","order":4}]},{"id":"seed_83233c3c4eba3d8393e4b5df2a901c2f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"RL Applications and Challenges","order":6,"contentType":"LEARNING","contentSlug":"rl-applications-and-challenges","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c91f7b85dbf8fb68a27f7983fd635a77","title":"Game Playing: RL's Proving Ground","order":0},{"id":"seed_6303b2baf45452eecabed6200b85c5b8","title":"Robotics: RL Meets the Physical World","order":1},{"id":"seed_adf80c63030036e2db509956663d62a9","title":"Sample Efficiency: Learning from Limited Experience","order":2},{"id":"seed_9b6b38c518d11a6aeffce2ed0ffd26bf","title":"Exploration: Discovering Rewarding Behaviors","order":3},{"id":"seed_fe67e4c0bbb686c5e8347ec09e90513f","title":"Reward Engineering: Specifying What We Want","order":4}]},{"id":"seed_538da3bc0ccbd37d3fab29f08eb07443","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Intra-Group Advantage Normalization for Policy Training","order":7,"contentType":"PROBLEM","contentSlug":"group-relative-advantage-computation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_f11572f4df3cd111b29c1115c8d584f7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 39: Reinforcement Learning","sectionOrder":53,"title":"Unbiased Policy Divergence Estimation for Regularized Optimization","order":8,"contentType":"PROBLEM","contentSlug":"policy-divergence-estimator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_8ab2ad29bcfe76d03a61e3fdfc68781b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Recommender System Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"recommender-system-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_da79aaad30c9ea311f912f1f9987d531","title":"Problem Formulation","order":0},{"id":"seed_7badc8822a2b15e80fbd40c01c367037","title":"Explicit vs Implicit Feedback","order":1},{"id":"seed_9f37ce8b28608b223711c6938c7552dd","title":"Cold Start Problem","order":2},{"id":"seed_05fe158c39262a24da982d68b162c785","title":"Evaluation Metrics","order":3},{"id":"seed_bfa867934341a6111dcc07ea78e4d2bf","title":"A/B Testing","order":4}]},{"id":"seed_7b67e7eda540fa724d88ee493898b69c","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Collaborative Filtering","order":2,"contentType":"LEARNING","contentSlug":"collaborative-filtering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8e114f4d826ccf0ab985de00191f339d","title":"User-based Collaborative Filtering","order":0},{"id":"seed_1a287e127ed7b797307b25696683b668","title":"Item-based Collaborative Filtering","order":1},{"id":"seed_fec56910667f6081593a37b974de8fd4","title":"Similarity Measures","order":2},{"id":"seed_4212728e713835369369a99532daa71e","title":"Neighborhood Methods","order":3},{"id":"seed_dc4ebe51624cefebfa81bdbc180cb66b","title":"Scalability","order":4}]},{"id":"seed_8b451bfeded3345408e779b5bc2b7fb1","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Matrix Factorization for Recommendations","order":3,"contentType":"LEARNING","contentSlug":"matrix-factorization","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5f57691eb9a54464572ab37699240050","title":"Latent Factor Models","order":0},{"id":"seed_82b8c2b2e7a643aa865b8510c5f457fd","title":"SVD and SVD++","order":1},{"id":"seed_d6452e1d4eec53e60eab0f44b08b0f55","title":"ALS Optimization","order":2},{"id":"seed_cfc7c00993c26515a09b4861d9e34132","title":"Regularization","order":3},{"id":"seed_cc24f9e943ef67af26b2d13aee468672","title":"Implicit Feedback","order":4}]},{"id":"seed_19703d4984d6329646468830d58dec7f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Content-Based Methods","order":4,"contentType":"LEARNING","contentSlug":"content-based-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_942fca4739758c626edbae77a46390af","title":"Item Representations","order":0},{"id":"seed_f5f88433deb2d6eda32b34a0068f85ea","title":"User Profiles","order":1},{"id":"seed_e8cd537403dc19d3a8a734a2e7e35333","title":"TF-IDF and Embeddings","order":2},{"id":"seed_524f46aefda3f7e487ba65082478cc48","title":"Hybrid Approaches","order":3},{"id":"seed_c2b0132208f95f732d98558c2b08d1dd","title":"Knowledge-Based Methods","order":4}]},{"id":"seed_2ab9a2c09cd93ba87112ecc170e53513","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Deep Learning for Recommendations","order":5,"contentType":"LEARNING","contentSlug":"deep-learning-for-recommendations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cd55383761648ae19ade5e00fdbdeaaa","title":"Neural Collaborative Filtering","order":0},{"id":"seed_796dda7197932d491d8674bcb2373460","title":"Autoencoders for Recommendations","order":1},{"id":"seed_41ee506f7daa5b266042f506e36820f2","title":"Sequence Models for Recommendations","order":2},{"id":"seed_e4035df4a7dae23acb7cd09c527dbb74","title":"Graph Neural Networks for Recommendations","order":3},{"id":"seed_d49849fa3f7188ee49f9f2628eefbd03","title":"Two-Tower Models","order":4}]},{"id":"seed_2af5ea0db1dad6b70b5104436968ca34","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 40: Recommendation Systems","sectionOrder":54,"title":"Production Considerations","order":6,"contentType":"LEARNING","contentSlug":"production-considerations","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d0a185b33bbcdd5c756b28e1b4c8b964","title":"Scalability","order":0},{"id":"seed_7bf671fd3047f87c11088ae13eefdba4","title":"Real-time Requirements","order":1},{"id":"seed_dcadbbc9078fdaad2e7b2f925c11f657","title":"Diversity and Fairness","order":2},{"id":"seed_cd36de3d99810f70e3097470e5464818","title":"Exploration-Exploitation","order":3},{"id":"seed_bb173a04f8c5ba1044d6393e46cd6d45","title":"Feedback Loops","order":4}]},{"id":"seed_388dd9ab336383cfe8b60203b6a21a39","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Engineering Mastery","order":1,"contentType":"LEARNING","contentSlug":"feature-engineering-mastery","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_56dd58a33d5534516199c7580e841f63","title":"Feature Types","order":0},{"id":"seed_842f4a11ba72179bd359958a69c32a08","title":"Domain Knowledge","order":1},{"id":"seed_0e74c9a08deb29e70b8fc92ba3fe99ed","title":"Interaction Features","order":2},{"id":"seed_a46bdb1443418ed68ebb0bc3223d5a9d","title":"Time-Based Features","order":3},{"id":"seed_09805d9c32a742bd2a525f9770161506","title":"Text and Categorical","order":4}]},{"id":"seed_5de18599b9e5181b808780cb9c304da9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Feature Selection Theory","order":2,"contentType":"LEARNING","contentSlug":"feature-selection-theory","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ac96756fe1d66bb7a3dccb4937954afc","title":"Filter Methods","order":0},{"id":"seed_ab79381fc456ad8c97dfdf7bcc88e2d0","title":"Wrapper Methods","order":1},{"id":"seed_cd41c3c79e7e50fd872cb73cfc07fa95","title":"Embedded Methods","order":2},{"id":"seed_392bf51ca8608e808c255ac167e9a796","title":"Stability Selection","order":3},{"id":"seed_ff2defccc0fa65581937fe462a85cd0c","title":"Feature Selection Bias","order":4}]},{"id":"seed_497f3f5dbb61fb7014f109d9f0eb336b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Text Feature Engineering: N-grams","order":3,"contentType":"LEARNING","contentSlug":"text-feature-engineering-ngrams","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3dd8fb7521fc7de44f84462e5a4123a3","title":"Unigrams: The Foundation of Text Feature Engineering","order":0},{"id":"seed_33d61480d27dcd4b92e02bbd8e462e39","title":"Bigrams: Capturing Local Context and Word Pairs","order":1},{"id":"seed_8a5d5e51dba41c7acf29ee19b0efcd45","title":"N-grams: The General Framework for Sequential Text Features","order":2},{"id":"seed_f3c436df144b4698d3527fe8e631cb4b","title":"Character N-grams: Sub-Word Patterns for Robust Text Features","order":3},{"id":"seed_8a62d2081dd9bfaa4e98b712225c6281","title":"N-gram Tradeoffs: A Decision Framework for Text Feature Engineering","order":4}]},{"id":"seed_7f90aa61bc4fb373dafbad502a5f0fd6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Data Normalization Techniques: Z-Score and Min-Max Scaling","order":4,"contentType":"PROBLEM","contentSlug":"feature-scaling-normalization","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_741941446f08f63cd865ccae73a1a71f","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Categorical Binary Encoding","order":5,"contentType":"PROBLEM","contentSlug":"categorical-binary-encoding","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_7df14192fe8a798ae765e3b012cede59","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering And Selection","sectionOrder":55,"title":"Ordered Polynomial Feature Generator","order":6,"contentType":"PROBLEM","contentSlug":"ordered-polynomial-feature-generator","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_842f5163e5a439bbac1ef1918b42633a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Automated Feature Engineering","order":1,"contentType":"LEARNING","contentSlug":"automated-feature-engineering","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_3397af7317b5277d5ff7122ec986f116","title":"Featuretools: The Foundation of Automated Feature Engineering","order":0},{"id":"seed_c93720908e81eeea4f853b8ba2efab46","title":"Feature Generation: The Complete Taxonomy","order":1},{"id":"seed_7f8e4e3a35e3861f8fc02a717cd30018","title":"Deep Feature Synthesis: The Algorithm Behind Automated FE","order":2},{"id":"seed_e4c80a37f7bee769e6fabbd9ae074e0d","title":"Feature Evaluation: Assessing and Selecting Quality Features","order":3},{"id":"seed_62aabd701415cdeb8d9072e5844a63a9","title":"Computational Considerations: Scaling Automated Feature Engineering","order":4}]},{"id":"seed_81d80261d64ffea6e4533de78d2b9dd0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Stores: The Foundation of Production ML Feature Management","order":2,"contentType":"LEARNING","contentSlug":"feature-stores","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_787216c95406c4c50e1cccdad4f7e772","title":"Feature Store Concept","order":0},{"id":"seed_62ca6df0b94f8119b40371a69912332b","title":"Feast Architecture","order":1},{"id":"seed_326812b4f0808df4c9ebb4bcda549256","title":"Online vs Offline Stores","order":2},{"id":"seed_5353da15321599c127209aa5f6e4ca9d","title":"Feature Reuse","order":3},{"id":"seed_713c7e8ee09618960af8874d2b6ad9ac","title":"Data Consistency","order":4}]},{"id":"seed_f944d98b4893ec36ff4f28076308eeb2","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Feature Transformation Pipelines","order":3,"contentType":"LEARNING","contentSlug":"feature-transformation-pipelines","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7b68de2b1bf371c70013fba46412323f","title":"Scikit-learn Pipelines","order":0},{"id":"seed_35aff504fdc34a1c33ff6a3dd3b0df4b","title":"Column Transformers","order":1},{"id":"seed_2d3c19ab28d6a7d8592f4baff3be2871","title":"Custom Transformers","order":2},{"id":"seed_c138507a800c9c01caf55ae68aa95d44","title":"Pipeline Serialization","order":3},{"id":"seed_4aabbdcc5ad82c6b1dadf8ccd7595c30","title":"Production Deployment","order":4}]},{"id":"seed_cc3d8ae2e097a16d9fcc802ad211391b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"High-Cardinality Features","order":4,"contentType":"LEARNING","contentSlug":"high-cardinality-features","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e8a9692fe85c336e537a31efc06aa276","title":"Categorical Encoding","order":0},{"id":"seed_3a5f6e41dc69b628e8a3d3bcb0d0a2cb","title":"Target Encoding","order":1},{"id":"seed_b6f90e488863398b5e581f6af6a8aff0","title":"Embedding Layers","order":2},{"id":"seed_9c0e82c9a6af106f7c4653208d6dca98","title":"Hash Encoding","order":3},{"id":"seed_d3071d36c8bdf27dd53adbc50ee95dc5","title":"Handling Novel Categories","order":4}]},{"id":"seed_a27695ebceaabefd2a3d7ab9d65fb829","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering - Basics","order":5,"contentType":"LEARNING","contentSlug":"text-feature-engineering-basics","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_a37470f2978780243aa1adee401dd2c5","title":"Bag of Words (BoW)","order":0},{"id":"seed_0a8c02fb8aa1c784bb28cbe17b6dc295","title":"Term Frequency","order":1},{"id":"seed_d411547d24c6279d027de0ee3e4338d4","title":"Vocabulary Construction","order":2},{"id":"seed_64125aa5be53f39a763f024357df3c53","title":"Sparse Representation","order":3},{"id":"seed_1de37c635dfbb87c73249559e726e8fe","title":"BoW Limitations","order":4}]},{"id":"seed_1c77cb034b5085130e3be375725cf1a3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Feature Engineering: TF-IDF","order":6,"contentType":"LEARNING","contentSlug":"text-feature-engineering-tfidf","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9c7581934f8e61cf171b019d4a5f2959","title":"Term Frequency (TF)","order":0},{"id":"seed_3539671e8054f66d418cf5db15c6b707","title":"Inverse Document Frequency (IDF)","order":1},{"id":"seed_25afc0c3d4c696a1952efac83025b717","title":"Why Use Logarithm in IDF","order":2},{"id":"seed_682112f47678f276744f7a07d503cbd7","title":"TF-IDF Weighting","order":3},{"id":"seed_bf9dd12b85180b591ca39e96b13e90f4","title":"Normalization","order":4}]},{"id":"seed_7491c6ce5e01ba00b2b823909fe0d32a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Text Preprocessing: Transforming Raw Text for Machine Learning","order":7,"contentType":"LEARNING","contentSlug":"text-preprocessing","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7cbadb21e2b31a9dd0315fdd172001ad","title":"Tokenization: The Foundation of Text Processing","order":0},{"id":"seed_0df76009024a8c244f8e4edebbf19916","title":"Stop-word Removal: Filtering Noise from Signal","order":1},{"id":"seed_4811497501c2a3593419f69a3fb73bb1","title":"Stemming: Rule-Based Root Extraction","order":2},{"id":"seed_a1c86228d0e56e4e3c9561a164d59970","title":"Lemmatization: Linguistically-Informed Normalization","order":3},{"id":"seed_6dfb9fde8664d1ebcc8d5a84acd886c4","title":"Case Normalization: Unifying Text Representation","order":4},{"id":"seed_08f39130a486e27c3f000e2b77b31116","title":"Punctuation Handling: Managing Text Delimiters","order":5}]},{"id":"seed_7980b68a9f4f240de52a46472d229990","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 41: Feature Engineering Selection","sectionOrder":56,"title":"Word Embeddings","order":8,"contentType":"LEARNING","contentSlug":"word-embeddings","description":null,"estimatedMins":60,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_313949bc1ac98d9926b8880ce864c9e5","title":"Word2Vec: Skip-gram and CBOW Architectures","order":0},{"id":"seed_b786f03991793dd229cb933d7a88cd32","title":"Average Word2Vec: Document Representation through Vector Averaging","order":1},{"id":"seed_ca3e82787e5cbde8ee221cba1109b267","title":"TF-IDF Weighted Word2Vec: Principled Word Importance Weighting","order":2},{"id":"seed_373581ffe928200559170c7911220d40","title":"GloVe: Global Vectors for Word Representation","order":3},{"id":"seed_af8fa64c1a2408b2174a796a5acbbbe4","title":"FastText: Subword Embeddings for Morphological Richness and OOV Handling","order":4},{"id":"seed_94485eed4514fa4e4a8dd85c63971ece","title":"Pre-trained Embeddings: Practical Strategies for Real-World NLP","order":5}]},{"id":"seed_a9d7121a5834d039ddce6eec079e2e25","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Overview","order":1,"contentType":"LEARNING","contentSlug":"automl-overview","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7542e71a8c159295b15fb3eac6b2a0ab","title":"AutoML Motivation","order":0},{"id":"seed_96c506c7dde6acf11f05452efb550328","title":"What to Automate","order":1},{"id":"seed_21de258b506a5c1e17ce473db86e6c0b","title":"Search Space","order":2},{"id":"seed_145d6e1bc19abcc0c02d60f0b8fe3269","title":"Search Strategies","order":3},{"id":"seed_3c522bdcee806d975c528292d4c8f50e","title":"Evaluation Strategies","order":4}]},{"id":"seed_4822c3e211b027bbc0b9e39d013cd2bd","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Preprocessing","order":2,"contentType":"LEARNING","contentSlug":"automated-preprocessing","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_b9a2eec6d3ae1b6319657e5afd063c63","title":"Missing Value Handling","order":0},{"id":"seed_b4d95e724ff721c84b4eb4835277047d","title":"Encoding Selection","order":1},{"id":"seed_73cd4522b4aa2d8f73cb6951ffc9dacd","title":"Scaling Selection","order":2},{"id":"seed_0106037f6b6a431abdffebfddeb0d303","title":"Feature Selection","order":3},{"id":"seed_8720af1f18547a30a1fb6ce89f8e22ab","title":"Data Cleaning","order":4}]},{"id":"seed_ae082beb489cc334aefa7f1daded438d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Automated Model Selection","order":3,"contentType":"LEARNING","contentSlug":"automated-model-selection","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_ffaaa57d1087b8544b058d22bc8da3db","title":"Algorithm Selection","order":0},{"id":"seed_2626021d42d4fcef0932b13fa771dd10","title":"Combined Algorithm Selection and Hyperparameter Optimization (CASH)","order":1},{"id":"seed_ab0c7276e5022476f0e56e2915e5f3a6","title":"Meta-Learning for Selection","order":2},{"id":"seed_4290041bca027ca9130d65afdd0f9af5","title":"Warm Starting","order":3},{"id":"seed_433dc419437b5c80cb73f7d4a5f6346e","title":"Portfolio Methods","order":4}]},{"id":"seed_e1b7d2e67067c90c33a877fa1c26d3e6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"Neural Architecture Search","order":4,"contentType":"LEARNING","contentSlug":"neural-architecture-search","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_01d895ee2a72d00d4df87806cd3162fb","title":"NAS Overview","order":0},{"id":"seed_784450ade551a425c852fa5da276b72a","title":"Search Space Design","order":1},{"id":"seed_d31f73220a65fb1077a8543efafc3ce5","title":"Search Strategies","order":2},{"id":"seed_18ece3a85ea71f09590d4427d1749ba0","title":"Weight Sharing","order":3},{"id":"seed_fd66bdc67d04569dd6b120032330471f","title":"Efficient NAS","order":4}]},{"id":"seed_47b36d588fcad669b42eea822d760f0a","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Systems","order":5,"contentType":"LEARNING","contentSlug":"automl-systems","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_999d801f74d27b98ee8a965144b01abd","title":"Auto-sklearn: Automated Machine Learning Through Meta-Learning","order":0},{"id":"seed_40e33223b5d1d10832c94812ce3efd72","title":"AutoGluon: Multi-Layer Stack Ensembling for State-of-the-Art AutoML","order":1},{"id":"seed_944f7b937af1f07a3becf8f516d89f79","title":"H2O AutoML: Enterprise-Grade Automated Machine Learning","order":2},{"id":"seed_bb54014702c4990361531ddb2b7c1bd7","title":"Google Cloud AutoML: Managed AutoML at Scale","order":3},{"id":"seed_bb4cd84056815877f0628e9701ac59a5","title":"AutoML System Comparison: A Decision Framework","order":4}]},{"id":"seed_cb9a45dc1c54e73311b24cd928b3ab69","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 42: Automl Neural Architecture Search","sectionOrder":57,"title":"AutoML Best Practices","order":6,"contentType":"LEARNING","contentSlug":"automl-best-practices","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_1e56c899d3be2441d2775eb02d7c03a2","title":"When to Use AutoML","order":0},{"id":"seed_cf58a8ce44274360741fb76ba3fb5535","title":"Resource Budgets","order":1},{"id":"seed_ffbda19dd65097600ca4aa701c2d781b","title":"Constraint Handling","order":2},{"id":"seed_c730d91ae8cd4cb24d21f9b012b6d48c","title":"Explainability","order":3},{"id":"seed_4abc81b3e277d736edc8dc1c1ef274f6","title":"Production Deployment","order":4}]},{"id":"seed_1458bdc7f12dad04db575fa4a57fe870","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability And Fairness","sectionOrder":58,"title":"Feature Attribution Methods","order":1,"contentType":"LEARNING","contentSlug":"feature-attribution-methods","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_7f8b760e1525ac7fa4d8e4dfba277d6c","title":"Permutation Importance","order":0},{"id":"seed_f71c333cf01a9d507cf0aaaf3b5e2671","title":"SHAP Values","order":1},{"id":"seed_e8a6176cfd8277ab46a521a89d831054","title":"Shapley Theory","order":2},{"id":"seed_cceab90cdc080e7cac17ef85c213768d","title":"LIME","order":3},{"id":"seed_243361fd052cdfc38c170d028894c46e","title":"Integrated Gradients","order":4}]},{"id":"seed_0f88251e409f204b5dcec58e017412d4","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Interpretability Fundamentals","order":1,"contentType":"LEARNING","contentSlug":"interpretability-fundamentals","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d5ce117891789cb31c761cb32e70dab6","title":"Why Interpretability","order":0},{"id":"seed_50aa5d581152c5103bc9f5296e2eba99","title":"Intrinsic vs Post-hoc Interpretability","order":1},{"id":"seed_2211b3c8d0505aab86ecba7d5b57790d","title":"Local vs Global Interpretability","order":2},{"id":"seed_6d99987f423504264f7930d36b866a89","title":"Model-Specific vs Model-Agnostic Interpretability","order":3},{"id":"seed_dc43bb2875717a67a46a9d37cbd4eed9","title":"Accuracy-Interpretability Tradeoff","order":4}]},{"id":"seed_09729918b026512bb25f63a10b88afa7","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Model-Specific Interpretability","order":2,"contentType":"LEARNING","contentSlug":"model-specific-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_5fc6859ed2fd7f82a50090fbd65fbcb6","title":"Linear Model Coefficients","order":0},{"id":"seed_cb22f4ff362a904d45361e7eacaff58f","title":"Tree Visualization","order":1},{"id":"seed_399396af2ffc07e3eb4e424c07bf76f0","title":"Attention Visualization","order":2},{"id":"seed_f70b2a429f82e5ea1355baddb1d48f78","title":"Saliency Maps","order":3},{"id":"seed_f724cc23b5ba2d68bdada80937126b14","title":"Concept Activation Vectors","order":4}]},{"id":"seed_6377599b4a10d0e02c1cfc2dc7aaf743","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Fairness in Machine Learning","order":3,"contentType":"LEARNING","contentSlug":"fairness-in-ml","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_309fc9b026a82738fd0ffd4efa762dea","title":"Fairness Definitions","order":0},{"id":"seed_c9669f3fd3f2489be7876c4c844ad879","title":"Protected Attributes","order":1},{"id":"seed_5dfa7ccb832098884a3318106327aac1","title":"Disparate Impact","order":2},{"id":"seed_47c407bcff666dac87f5a0f33fbb3fa8","title":"Equality of Opportunity","order":3},{"id":"seed_3403c6bb875454aafb8f54dba83207db","title":"Fairness Metrics","order":4}]},{"id":"seed_4951de29801ee8a44a8759a1597460f0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Bias Detection and Mitigation","order":4,"contentType":"LEARNING","contentSlug":"bias-detection-mitigation","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_97ad248d1c4ad427a7efb88eecf2ba04","title":"Bias Sources","order":0},{"id":"seed_8cc2506a8e01f4c564565f2f85169e9a","title":"Pre-processing Methods","order":1},{"id":"seed_11adbd5188d9672d8890ca7e1170c209","title":"In-processing Methods","order":2},{"id":"seed_1330854c21f83c257eef1c48b773a2f5","title":"Post-processing Methods","order":3},{"id":"seed_ca4190b0e9d08dc9b766f7067e25bad0","title":"Fairness-Accuracy Tradeoffs","order":4}]},{"id":"seed_2a221429a71db5df8da0275ce4697a10","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 43: Ml Interpretability Fairness","sectionOrder":59,"title":"Practical Interpretability","order":5,"contentType":"LEARNING","contentSlug":"practical-interpretability","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_627159c6c30aac4e0a4ef9b4f107f7fe","title":"Stakeholder Communication","order":0},{"id":"seed_a9b44d22fcc5c785e4175062c1f9106d","title":"Regulatory Requirements","order":1},{"id":"seed_d752805cbccb81d2a11d7a99a2f0be54","title":"Model Cards","order":2},{"id":"seed_3458ab9c5da87704e37311b865e14968","title":"Documentation","order":3},{"id":"seed_47422c41a26465d8552f9efcb4e3bc8e","title":"Auditing","order":4}]},{"id":"seed_a4064079bf9971ca27d16886141b8871","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Foundation Models","order":1,"contentType":"LEARNING","contentSlug":"foundation-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e5ff6409aa980ac554efadd1e94ee4e7","title":"Scale in Machine Learning","order":0},{"id":"seed_a911ffc9a22b3e53bb616705a7199e43","title":"Emergent Capabilities","order":1},{"id":"seed_b76d73e27f6c36569f3a09eadce4df8c","title":"GPT and Large Language Models","order":2},{"id":"seed_2096a2ed0f835a28795305e6de100b0d","title":"Multimodal Models","order":3},{"id":"seed_1922260f859a2dcd43e00f4f2dd4a801","title":"Foundation Model Paradigm","order":4}]},{"id":"seed_50bdd845bc425620c7bd09e44d4f5827","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Large Language Models","order":2,"contentType":"LEARNING","contentSlug":"large-language-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_8ccbfbacf3f73e9fc2bb5c6fa02d84ec","title":"Transformer Scaling","order":0},{"id":"seed_a184494d3f9595754b2c0d871563a473","title":"Pre-training Objectives","order":1},{"id":"seed_f9a3ade1f26bafc774a15cb607c99b93","title":"Instruction Tuning","order":2},{"id":"seed_856e49265ee859098fa08fe027599d6e","title":"RLHF (Reinforcement Learning from Human Feedback)","order":3},{"id":"seed_8d48cf51c9b629aacdb2f8d16dc69518","title":"Prompting and In-Context Learning","order":4}]},{"id":"seed_db062e2abf514009df75d477ba2962e0","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Multimodal Learning: Bridging Vision, Language, and Beyond","order":3,"contentType":"LEARNING","contentSlug":"multimodal-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_21cfd3cc39b3c00fd538566a2b2b970d","title":"Vision-Language Models: Foundations and Architectures","order":0},{"id":"seed_a458d009e0614689105bba3999e3a5a9","title":"CLIP and DALL-E: Revolutionary Vision-Language Models","order":1},{"id":"seed_aaeb719046c90693945174d0b489929e","title":"Audio-Visual Learning: When AI Sees and Hears","order":2},{"id":"seed_2439a1fe23c39c5f86d9f772f8aabbd8","title":"Cross-Modal Retrieval: Searching Across Modalities","order":3},{"id":"seed_70ca6fa7f6f2c91bea9fa02991ff0796","title":"Unified Multimodal Architectures: One Model, Many Modalities","order":4}]},{"id":"seed_e885aaae2eb8f75705604e9d2293f2ec","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Federated Learning","order":4,"contentType":"LEARNING","contentSlug":"federated-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.304Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_cb2594c497a2ffbe2aa36bb7c6cc095b","title":"Distributed Training: Foundations of Federated Learning","order":0},{"id":"seed_884e5881dae8edbe5a44143acdfe27bf","title":"Privacy Preservation: Rigorous Privacy Guarantees in Federated Learning","order":1},{"id":"seed_4fd909395316a45c9f711ada8942fe27","title":"Communication Efficiency: Reducing Bandwidth in Federated Learning","order":2},{"id":"seed_5b22ed855569eac5c10fb228d06b4879","title":"Heterogeneous Data: Tackling Non-IID Distributions in Federated Learning","order":3},{"id":"seed_4a4b787bcc3975702d21898a316f2e91","title":"Federated Optimization: Algorithms for Decentralized Training","order":4}]},{"id":"seed_f6da44328600f666e7d9a74d1c32b432","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Continual Learning: Learning Without Forgetting","order":5,"contentType":"LEARNING","contentSlug":"continual-learning","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_92a78316ca1bb16c2f40fab2d2518383","title":"Catastrophic Forgetting: The Core Challenge","order":0},{"id":"seed_69b6f03d2835519df087e54057261a58","title":"Regularization Approaches to Continual Learning","order":1},{"id":"seed_42fd7700cdd956638d6ab3cbfdf81ac8","title":"Replay Methods for Continual Learning","order":2},{"id":"seed_2debd5b4dce66b05eb76895a6d0c99ea","title":"Dynamic Architectures for Continual Learning","order":3},{"id":"seed_73f622be84d768dde27c55e1448fef3c","title":"Evaluation Protocols for Continual Learning","order":4}]},{"id":"seed_3efeb1d46a6097c7b10ddb9525c4b581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 44: Research Frontiers","sectionOrder":60,"title":"Emerging Directions in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"emerging-directions","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_c26a73ba96e34b13cd1f6f890379cea1","title":"Neurosymbolic AI","order":0},{"id":"seed_883ebe97d9e2af44700fe4162805078a","title":"Causal Machine Learning","order":1},{"id":"seed_c4e6f3699d8f21e860a45eb1eccab744","title":"World Models","order":2},{"id":"seed_c3f133751013f2c0c6c18cd5b8d11d14","title":"AI Safety","order":3},{"id":"seed_9bdbf916201809317c606d22144fee34","title":"Efficient ML","order":4}]},{"id":"seed_723cca038a2c40f84476ae0e31ab18e3","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML System Design","order":1,"contentType":"LEARNING","contentSlug":"ml-system-design","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_9c02b1cac102d1618b5d2ec9d3acba59","title":"Requirements Gathering","order":0},{"id":"seed_4dc28980257492a88544dbbe86e73b19","title":"Data Pipeline Design","order":1},{"id":"seed_62a45e62e8b80ee0447ecd615fa712c0","title":"Model Architecture","order":2},{"id":"seed_b7258f0e68a594a5a66f52270fa9899a","title":"Serving Infrastructure","order":3},{"id":"seed_0732cd35ca60ac2d3904f7f048471665","title":"Monitoring and Maintenance","order":4}]},{"id":"seed_dbfa0d7a5ff41a228873f58339420ef9","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Debugging ML Models","order":2,"contentType":"LEARNING","contentSlug":"debugging-ml-models","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_d9d2a99058adc397a751811416595e5f","title":"Training Debugging","order":0},{"id":"seed_4271b4ae3eaaa79f36aa687080b09aba","title":"Data Debugging","order":1},{"id":"seed_86cf1003e649a0ded1b3bf5b996cf595","title":"Model Debugging","order":2},{"id":"seed_f226e71a2171e33f686c6786b4d49392","title":"Performance Debugging","order":3},{"id":"seed_fd41a6a1a89a4d5ddcc8aa0a4dedc876","title":"Systematic Debugging","order":4}]},{"id":"seed_29daeedf46dc1d31c9070e587276c581","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Project Management","order":3,"contentType":"LEARNING","contentSlug":"ml-project-management","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_877ea1e28d3391ee7bbb06ebedda76b6","title":"Problem Scoping","order":0},{"id":"seed_ef3cfcdc33e9e1e026cfd75d00380c8f","title":"Data Collection Planning","order":1},{"id":"seed_25075dce25ecddadca46f876a40d36f0","title":"Experiment Management","order":2},{"id":"seed_9e6bfb02646e5dca43d4dcc3111e7182","title":"Iteration Strategies","order":3},{"id":"seed_4ff1ad1d9656b082b07548425a47280a","title":"Launch Criteria","order":4}]},{"id":"seed_800ab48cf1731556a258ea5ec18b78f6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Reading Research Papers","order":4,"contentType":"LEARNING","contentSlug":"reading-research-papers","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_e4a60c573f82838aac6f77ba4858a2c2","title":"Paper Structure","order":0},{"id":"seed_1ff1780965108efee6996e30b65aeebf","title":"Critical Reading","order":1},{"id":"seed_bf84ef490bfeb38c9d31de6585c6bb9f","title":"Reproducing Results","order":2},{"id":"seed_db748e29a14347a7ef6d817001e9cdb7","title":"Staying Current","order":3},{"id":"seed_0c63aa37941655ef50378149ce6ee0e9","title":"Research Taste","order":4}]},{"id":"seed_f292b4c2034d13213b3e7c675b18f765","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"ML Interviews: Complete Preparation Guide","order":5,"contentType":"LEARNING","contentSlug":"ml-interviews","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_4cf930be608fbe9c6aca6e6eb103f2f3","title":"ML Interview Types: A Complete Taxonomy","order":0},{"id":"seed_1b7e561e9cbcadbd9cb99da6b2645565","title":"Technical Preparation: A Strategic Approach","order":1},{"id":"seed_49c4bddac8288cb3ac3494b82e63b4ea","title":"Coding Interviews: Mastery Guide for ML Roles","order":2},{"id":"seed_7b925848fb34013f6b55161466bc4847","title":"ML Design Interviews: System Architecture Mastery","order":3},{"id":"seed_e476ec333b423888c3ff6981da80f6ed","title":"Common ML Interview Questions: The Essential Reference","order":4}]},{"id":"seed_632dc2189cf690119e3942c3dfa69d8b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Career Development in Machine Learning","order":6,"contentType":"LEARNING","contentSlug":"career-development","description":null,"estimatedMins":50,"createdAt":"2026-10-15T17:47:45.337Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[{"id":"seed_26c147dafb9cdd140c576d97b69cf6c3","title":"ML Career Paths","order":0},{"id":"seed_1d664a155647c95db0e8933179063636","title":"Skill Development","order":1},{"id":"seed_eed0cee99e2d5bf6562ca0f1c541be58","title":"Building Portfolio","order":2},{"id":"seed_61b1f72e33d35a7e5c26127c6fcb33dc","title":"Community Engagement","order":3},{"id":"seed_1be2e204910fc41848aa81e200d02788","title":"Continuous Learning","order":4}]},{"id":"seed_39cac80dede8b891307a5865480e576b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Dataset Batch Generator for Training Pipelines","order":7,"contentType":"PROBLEM","contentSlug":"dataset-batch-generator","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_76bec3396a0c10a07b961c0659f31f5b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Performance Metrics Calculator","order":8,"contentType":"PROBLEM","contentSlug":"inference-performance-metrics","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_d8bc35e4e84cf3a27ee560839206c155","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Chapter 45: Ml Engineering Career","sectionOrder":61,"title":"Inference Request Batching","order":9,"contentType":"PROBLEM","contentSlug":"inference-request-batching","description":null,"estimatedMins":30,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_07789f50da99325787e488c27c2f335e","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Synchronized Data Permutation","order":1,"contentType":"PROBLEM","contentSlug":"synchronized-data-permutation","description":null,"estimatedMins":20,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_0818b627401c3528be0e63d2b248925b","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"One bad dataset row consumes several quality allowances","order":2,"contentType":"REPO","contentSlug":"dataset-gate-failure-budget","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_76aa74612887d6b05ec63a158ebb9b68","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Count bad dataset rows once","order":3,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-failure-budget","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_82746decf240c4d206937e214136ffe6","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Published datasets differ from the snapshot that passed review","order":4,"contentType":"REPO","contentSlug":"dataset-gate-unchecked-publication","description":null,"estimatedMins":60,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]},{"id":"seed_6a6c35120fbb3874411dee3e7217348d","learningPathId":"seed_b2b87dff658cc600bb5313282ab109da","section":"Applied ML engineering: validate data releases","sectionOrder":62,"title":"Publish the rows captured by the quality run","order":5,"contentType":"CODE_REVIEW","contentSlug":"dataset-gate-review-unchecked-publication","description":null,"estimatedMins":35,"createdAt":"2026-10-17T16:02:59.096Z","updatedAt":"2026-10-17T16:28:32.434Z","pages":[]}]},"currentModule":"$45:props:state:queries:1:state:data:json:path:modules:84","prevModule":"$45:props:state:queries:1:state:data:json:path:modules:83","nextModule":"$45:props:state:queries:1:state:data:json:path:modules:85","allModules":"$45:props:state:queries:1:state:data:json:path:modules"},"meta":{"values":{"path.createdAt":["Date"],"path.updatedAt":["Date"],"path.modules.0.createdAt":["Date"],"path.modules.0.updatedAt":["Date"],"path.modules.1.createdAt":["Date"],"path.modules.1.updatedAt":["Date"],"path.modules.2.createdAt":["Date"],"path.modules.2.updatedAt":["Date"],"path.modules.3.createdAt":["Date"],"path.modules.3.updatedAt":["Date"],"path.modules.4.createdAt":["Date"],"path.modules.4.updatedAt":["Date"],"path.modules.5.createdAt":["Date"],"path.modules.5.updatedAt":["Date"],"path.modules.6.createdAt":["Date"],"path.modules.6.updatedAt":["Date"],"path.modules.7.createdAt":["Date"],"path.modules.7.updatedAt":["Date"],"path.modules.8.createdAt":["Date"],"path.modules.8.updatedAt":["Date"],"path.modules.9.createdAt":["Date"],"path.modules.9.updatedAt":["Date"],"path.modules.10.createdAt":["Date"],"path.modules.10.updatedAt":["Date"],"path.modules.11.createdAt":["Date"],"path.modules.11.updatedAt":["Date"],"path.modules.12.createdAt":["Date"],"path.modules.12.updatedAt":["Date"],"path.modules.13.createdAt":["Date"],"path.modules.13.updatedAt":["Date"],"path.modules.14.createdAt":["Date"],"path.modules.14.updatedAt":["Date"],"path.modules.15.createdAt":["Date"],"path.modules.15.updatedAt":["Date"],"path.modules.16.createdAt":["Date"],"path.modules.16.updatedAt":["Date"],"path.modules.17.createdAt":["Date"],"path.modules.17.updatedAt":["Date"],"path.modules.18.createdAt":["Date"],"path.modules.18.updatedAt":["Date"],"path.modules.19.createdAt":["Date"],"path.modules.19.updatedAt":["Date"],"path.modules.20.createdAt":["Date"],"path.modules.20.updatedAt":["Date"],"path.modules.21.createdAt":["Date"],"path.modules.21.updatedAt":["Date"],"path.modules.22.createdAt":["Date"],"path.modules.22.updatedAt":["Date"],"path.modules.23.createdAt":["Date"],"path.modules.23.updatedAt":["Date"],"path.modules.24.createdAt":["Date"],"path.modules.24.updatedAt":["Date"],"path.modules.25.createdAt":["Date"],"path.modules.25.updatedAt":["Date"],"path.modules.26.createdAt":["Date"],"path.modules.26.updatedAt":["Date"],"path.modules.27.createdAt":["Date"],"path.modules.27.updatedAt":["Date"],"path.modules.28.createdAt":["Date"],"path.modules.28.updatedAt":["Date"],"path.modules.29.createdAt":["Date"],"path.modules.29.updatedAt":["Date"],"path.modules.30.createdAt":["Date"],"path.modules.30.updatedAt":["Date"],"path.modules.31.createdAt":["Date"],"path.modules.31.updatedAt":["Date"],"path.modules.32.createdAt":["Date"],"path.modules.32.updatedAt":["Date"],"path.modules.33.createdAt":["Date"],"path.modules.33.updatedAt":["Date"],"path.modules.34.createdAt":["Date"],"path.modules.34.updatedAt":["Date"],"path.modules.35.createdAt":["Date"],"path.modules.35.updatedAt":["Date"],"path.modules.36.createdAt":["Date"],"path.modules.36.updatedAt":["Date"],"path.modules.37.createdAt":["Date"],"path.modules.37.updatedAt":["Date"],"path.modules.38.createdAt":["Date"],"path.modules.38.updatedAt":["Date"],"path.modules.39.createdAt":["Date"],"path.modules.39.updatedAt":["Date"],"path.modules.40.createdAt":["Date"],"path.modules.40.updatedAt":["Date"],"path.modules.41.createdAt":["Date"],"path.modules.41.updatedAt":["Date"],"path.modules.42.createdAt":["Date"],"path.modules.42.updatedAt":["Date"],"path.modules.43.createdAt":["Date"],"path.modules.43.updatedAt":["Date"],"path.modules.44.createdAt":["Date"],"path.modules.44.updatedAt":["Date"],"path.modules.45.createdAt":["Date"],"path.modules.45.updatedAt":["Date"],"path.modules.46.createdAt":["Date"],"path.modules.46.updatedAt":["Date"],"path.modules.47.createdAt":["Date"],"path.modules.47.updatedAt":["Date"],"path.modules.48.createdAt":["Date"],"path.modules.48.updatedAt":["Date"],"path.modules.49.createdAt":["Date"],"path.modules.49.updatedAt":["Date"],"path.modules.50.createdAt":["Date"],"path.modules.50.updatedAt":["Date"],"path.modules.51.createdAt":["Date"],"path.modules.51.updatedAt":["Date"],"path.modules.52.createdAt":["Date"],"path.modules.52.updatedAt":["Date"],"path.modules.53.createdAt":["Date"],"path.modules.53.updatedAt":["Date"],"path.modules.54.createdAt":["Date"],"path.modules.54.updatedAt":["Date"],"path.modules.55.createdAt":["Date"],"path.modules.55.updatedAt":["Date"],"path.modules.56.createdAt":["Date"],"path.modules.56.updatedAt":["Date"],"path.modules.57.createdAt":["Date"],"path.modules.57.updatedAt":["Date"],"path.modules.58.createdAt":["Date"],"path.modules.58.updatedAt":["Date"],"path.modules.59.createdAt":["Date"],"path.modules.59.updatedAt":["Date"],"path.modules.60.createdAt":["Date"],"path.modules.60.updatedAt":["Date"],"path.modules.61.createdAt":["Date"],"path.modules.61.updatedAt":["Date"],"path.modules.62.createdAt":["Date"],"path.modules.62.updatedAt":["Date"],"path.modules.63.createdAt":["Date"],"path.modules.63.updatedAt":["Date"],"path.modules.64.createdAt":["Date"],"path.modules.64.updatedAt":["Date"],"path.modules.65.createdAt":["Date"],"path.modules.65.updatedAt":["Date"],"path.modules.66.createdAt":["Date"],"path.modules.66.updatedAt":["Date"],"path.modules.67.createdAt":["Date"],"path.modules.67.updatedAt":["Date"],"path.modules.68.createdAt":["Date"],"path.modules.68.updatedAt":["Date"],"path.modules.69.createdAt":["Date"],"path.modules.69.updatedAt":["Date"],"path.modules.70.createdAt":["Date"],"path.modules.70.updatedAt":["Date"],"path.modules.71.createdAt":["Date"],"path.modules.71.updatedAt":["Date"],"path.modules.72.createdAt":["Date"],"path.modules.72.updatedAt":["Date"],"path.modules.73.createdAt":["Date"],"path.modules.73.updatedAt":["Date"],"path.modules.74.createdAt":["Date"],"path.modules.74.updatedAt":["Date"],"path.modules.75.createdAt":["Date"],"path.modules.75.updatedAt":["Date"],"path.modules.76.createdAt":["Date"],"path.modules.76.updatedAt":["Date"],"path.modules.77.createdAt":["Date"],"path.modules.77.updatedAt":["Date"],"path.modules.78.createdAt":["Date"],"path.modules.78.updatedAt":["Date"],"path.modules.79.createdAt":["Date"],"path.modules.79.updatedAt":["Date"],"path.modules.80.createdAt":["Date"],"path.modules.80.updatedAt":["Date"],"path.modules.81.createdAt":["Date"],"path.modules.81.updatedAt":["Date"],"path.modules.82.createdAt":["Date"],"path.modules.82.updatedAt":["Date"],"path.modules.83.createdAt":["Date"],"path.modules.83.updatedAt":["Date"],"path.modules.84.createdAt":["Date"],"path.modules.84.updatedAt":["Date"],"path.modules.85.createdAt":["Date"],"path.modules.85.updatedAt":["Date"],"path.modules.86.createdAt":["Date"],"path.modules.86.updatedAt":["Date"],"path.modules.87.createdAt":["Date"],"path.modules.87.updatedAt":["Date"],"path.modules.88.createdAt":["Date"],"path.modules.88.updatedAt":["Date"],"path.modules.89.createdAt":["Date"],"path.modules.89.updatedAt":["Date"],"path.modules.90.createdAt":["Date"],"path.modules.90.updatedAt":["Date"],"path.modules.91.createdAt":["Date"],"path.modules.91.updatedAt":["Date"],"path.modules.92.createdAt":["Date"],"path.modules.92.updatedAt":["Date"],"path.modules.93.createdAt":["Date"],"path.modules.93.updatedAt":["Date"],"path.modules.94.createdAt":["Date"],"path.modules.94.updatedAt":["Date"],"path.modules.95.createdAt":["Date"],"path.modules.95.updatedAt":["Date"],"path.modules.96.createdAt":["Date"],"path.modules.96.updatedAt":["Date"],"path.modules.97.createdAt":["Date"],"path.modules.97.updatedAt":["Date"],"path.modules.98.createdAt":["Date"],"path.modules.98.updatedAt":["Date"],"path.modules.99.createdAt":["Date"],"path.modules.99.updatedAt":["Date"],"path.modules.100.createdAt":["Date"],"path.modules.100.updatedAt":["Date"],"path.modules.101.createdAt":["Date"],"path.modules.101.updatedAt":["Date"],"path.modules.102.createdAt":["Date"],"path.modules.102.updatedAt":["Date"],"path.modules.103.createdAt":["Date"],"path.modules.103.updatedAt":["Date"],"path.modules.104.createdAt":["Date"],"path.modules.104.updatedAt":["Date"],"path.modules.105.createdAt":["Date"],"path.modules.105.updatedAt":["Date"],"path.modules.106.createdAt":["Date"],"path.modules.106.updatedAt":["Date"],"path.modules.107.createdAt":["Date"],"path.modules.107.updatedAt":["Date"],"path.modules.108.createdAt":["Date"],"path.modules.108.updatedAt":["Date"],"path.modules.109.createdAt":["Date"],"path.modules.109.updatedAt":["Date"],"path.modules.110.createdAt":["Date"],"path.modules.110.updatedAt":["Date"],"path.modules.111.createdAt":["Date"],"path.modules.111.updatedAt":["Date"],"path.modules.112.createdAt":["Date"],"path.modules.112.updatedAt":["Date"],"path.modules.113.createdAt":["Date"],"path.modules.113.updatedAt":["Date"],"path.modules.114.createdAt":["Date"],"path.modules.114.updatedAt":["Date"],"path.modules.115.createdAt":["Date"],"path.modules.115.updatedAt":["Date"],"path.modules.116.createdAt":["Date"],"path.modules.116.updatedAt":["Date"],"path.modules.117.createdAt":["Date"],"path.modules.117.updatedAt":["Date"],"path.modules.118.createdAt":["Date"],"path.modules.118.updatedAt":["Date"],"path.modules.119.createdAt":["Date"],"path.modules.119.updatedAt":["Date"],"path.modules.120.createdAt":["Date"],"path.modules.120.updatedAt":["Date"],"path.modules.121.createdAt":["Date"],"path.modules.121.updatedAt":["Date"],"path.modules.122.createdAt":["Date"],"path.modules.122.updatedAt":["Date"],"path.modules.123.createdAt":["Date"],"path.modules.123.updatedAt":["Date"],"path.modules.124.createdAt":["Date"],"path.modules.124.updatedAt":["Date"],"path.modules.125.createdAt":["Date"],"path.modules.125.updatedAt":["Date"],"path.modules.126.createdAt":["Date"],"path.modules.126.updatedAt":["Date"],"path.modules.127.createdAt":["Date"],"path.modules.127.updatedAt":["Date"],"path.modules.128.createdAt":["Date"],"path.modules.128.updatedAt":["Date"],"path.modules.129.createdAt":["Date"],"path.modules.129.updatedAt":["Date"],"path.modules.130.createdAt":["Date"],"path.modules.130.updatedAt":["Date"],"path.modules.131.createdAt":["Date"],"path.modules.131.updatedAt":["Date"],"path.modules.132.createdAt":["Date"],"path.modules.132.updatedAt":["Date"],"path.modules.133.createdAt":["Date"],"path.modules.133.updatedAt":["Date"],"path.modules.134.createdAt":["Date"],"path.modules.134.updatedAt":["Date"],"path.modules.135.createdAt":["Date"],"path.modules.135.updatedAt":["Date"],"path.modules.136.createdAt":["Date"],"path.modules.136.updatedAt":["Date"],"path.modules.137.createdAt":["Date"],"path.modules.137.updatedAt":["Date"],"path.modules.138.createdAt":["Date"],"path.modules.138.updatedAt":["Date"],"path.modules.139.createdAt":["Date"],"path.modules.139.updatedAt":["Date"],"path.modules.140.createdAt":["Date"],"path.modules.140.updatedAt":["Date"],"path.modules.141.createdAt":["Date"],"path.modules.141.updatedAt":["Date"],"path.modules.142.createdAt":["Date"],"path.modules.142.updatedAt":["Date"],"path.modules.143.createdAt":["Date"],"path.modules.143.updatedAt":["Date"],"path.modules.144.createdAt":["Date"],"path.modules.144.updatedAt":["Date"],"path.modules.145.createdAt":["Date"],"path.modules.145.updatedAt":["Date"],"path.modules.146.createdAt":["Date"],"path.modules.146.updatedAt":["Date"],"path.modules.147.createdAt":["Date"],"path.modules.147.updatedAt":["Date"],"path.modules.148.createdAt":["Date"],"path.modules.148.updatedAt":["Date"],"path.modules.149.createdAt":["Date"],"path.modules.149.updatedAt":["Date"],"path.modules.150.createdAt":["Date"],"path.modules.150.updatedAt":["Date"],"path.modules.151.createdAt":["Date"],"path.modules.151.updatedAt":["Date"],"path.modules.152.createdAt":["Date"],"path.modules.152.updatedAt":["Date"],"path.modules.153.createdAt":["Date"],"path.modules.153.updatedAt":["Date"],"path.modules.154.createdAt":["Date"],"path.modules.154.updatedAt":["Date"],"path.modules.155.createdAt":["Date"],"path.modules.155.updatedAt":["Date"],"path.modules.156.createdAt":["Date"],"path.modules.156.updatedAt":["Date"],"path.modules.157.createdAt":["Date"],"path.modules.157.updatedAt":["Date"],"path.modules.158.createdAt":["Date"],"path.modules.158.updatedAt":["Date"],"path.modules.159.createdAt":["Date"],"path.modules.159.updatedAt":["Date"],"path.modules.160.createdAt":["Date"],"path.modules.160.updatedAt":["Date"],"path.modules.161.createdAt":["Date"],"path.modules.161.updatedAt":["Date"],"path.modules.162.createdAt":["Date"],"path.modules.162.updatedAt":["Date"],"path.modules.163.createdAt":["Date"],"path.modules.163.updatedAt":["Date"],"path.modules.164.createdAt":["Date"],"path.modules.164.updatedAt":["Date"],"path.modules.165.createdAt":["Date"],"path.modules.165.updatedAt":["Date"],"path.modules.166.createdAt":["Date"],"path.modules.166.updatedAt":["Date"],"path.modules.167.createdAt":["Date"],"path.modules.167.updatedAt":["Date"],"path.modules.168.createdAt":["Date"],"path.modules.168.updatedAt":["Date"],"path.modules.169.createdAt":["Date"],"path.modules.169.updatedAt":["Date"],"path.modules.170.createdAt":["Date"],"path.modules.170.updatedAt":["Date"],"path.modules.171.createdAt":["Date"],"path.modules.171.updatedAt":["Date"],"path.modules.172.createdAt":["Date"],"path.modules.172.updatedAt":["Date"],"path.modules.173.createdAt":["Date"],"path.modules.173.updatedAt":["Date"],"path.modules.174.createdAt":["Date"],"path.modules.174.updatedAt":["Date"],"path.modules.175.createdAt":["Date"],"path.modules.175.updatedAt":["Date"],"path.modules.176.createdAt":["Date"],"path.modules.176.updatedAt":["Date"],"path.modules.177.createdAt":["Date"],"path.modules.177.updatedAt":["Date"],"path.modules.178.createdAt":["Date"],"path.modules.178.updatedAt":["Date"],"path.modules.179.createdAt":["Date"],"path.modules.179.updatedAt":["Date"],"path.modules.180.createdAt":["Date"],"path.modules.180.updatedAt":["Date"],"path.modules.181.createdAt":["Date"],"path.modules.181.updatedAt":["Date"],"path.modules.182.createdAt":["Date"],"path.modules.182.updatedAt":["Date"],"path.modules.183.createdAt":["Date"],"path.modules.183.updatedAt":["Date"],"path.modules.184.createdAt":["Date"],"path.modules.184.updatedAt":["Date"],"path.modules.185.createdAt":["Date"],"path.modules.185.updatedAt":["Date"],"path.modules.186.createdAt":["Date"],"path.modules.186.updatedAt":["Date"],"path.modules.187.createdAt":["Date"],"path.modules.187.updatedAt":["Date"],"path.modules.188.createdAt":["Date"],"path.modules.188.updatedAt":["Date"],"path.modules.189.createdAt":["Date"],"path.modules.189.updatedAt":["Date"],"path.modules.190.createdAt":["Date"],"path.modules.190.updatedAt":["Date"],"path.modules.191.createdAt":["Date"],"path.modules.191.updatedAt":["Date"],"path.modules.192.createdAt":["Date"],"path.modules.192.updatedAt":["Date"],"path.modules.193.createdAt":["Date"],"path.modules.193.updatedAt":["Date"],"path.modules.194.createdAt":["Date"],"path.modules.194.updatedAt":["Date"],"path.modules.195.createdAt":["Date"],"path.modules.195.updatedAt":["Date"],"path.modules.196.createdAt":["Date"],"path.modules.196.updatedAt":["Date"],"path.modules.197.createdAt":["Date"],"path.modules.197.updatedAt":["Date"],"path.modules.198.createdAt":["Date"],"path.modules.198.updatedAt":["Date"],"path.modules.199.createdAt":["Date"],"path.modules.199.updatedAt":["Date"],"path.modules.200.createdAt":["Date"],"path.modules.200.updatedAt":["Date"],"path.modules.201.createdAt":["Date"],"path.modules.201.updatedAt":["Date"],"path.modules.202.createdAt":["Date"],"path.modules.202.updatedAt":["Date"],"path.modules.203.createdAt":["Date"],"path.modules.203.updatedAt":["Date"],"path.modules.204.createdAt":["Date"],"path.modules.204.updatedAt":["Date"],"path.modules.205.createdAt":["Date"],"path.modules.205.updatedAt":["Date"],"path.modules.206.createdAt":["Date"],"path.modules.206.updatedAt":["Date"],"path.modules.207.createdAt":["Date"],"path.modules.207.updatedAt":["Date"],"path.modules.208.createdAt":["Date"],"path.modules.208.updatedAt":["Date"],"path.modules.209.createdAt":["Date"],"path.modules.209.updatedAt":["Date"],"path.modules.210.createdAt":["Date"],"path.modules.210.updatedAt":["Date"],"path.modules.211.createdAt":["Date"],"path.modules.211.updatedAt":["Date"],"path.modules.212.createdAt":["Date"],"path.modules.212.updatedAt":["Date"],"path.modules.213.createdAt":["Date"],"path.modules.213.updatedAt":["Date"],"path.modules.214.createdAt":["Date"],"path.modules.214.updatedAt":["Date"],"path.modules.215.createdAt":["Date"],"path.modules.215.updatedAt":["Date"],"path.modules.216.createdAt":["Date"],"path.modules.216.updatedAt":["Date"],"path.modules.217.createdAt":["Date"],"path.modules.217.updatedAt":["Date"],"path.modules.218.createdAt":["Date"],"path.modules.218.updatedAt":["Date"],"path.modules.219.createdAt":["Date"],"path.modules.219.updatedAt":["Date"],"path.modules.220.createdAt":["Date"],"path.modules.220.updatedAt":["Date"],"path.modules.221.createdAt":["Date"],"path.modules.221.updatedAt":["Date"],"path.modules.222.createdAt":["Date"],"path.modules.222.updatedAt":["Date"],"path.modules.223.createdAt":["Date"],"path.modules.223.updatedAt":["Date"],"path.modules.224.createdAt":["Date"],"path.modules.224.updatedAt":["Date"],"path.modules.225.createdAt":["Date"],"path.modules.225.updatedAt":["Date"],"path.modules.226.createdAt":["Date"],"path.modules.226.updatedAt":["Date"],"path.modules.227.createdAt":["Date"],"path.modules.227.updatedAt":["Date"],"path.modules.228.createdAt":["Date"],"path.modules.228.updatedAt":["Date"],"path.modules.229.createdAt":["Date"],"path.modules.229.updatedAt":["Date"],"path.modules.230.createdAt":["Date"],"path.modules.230.updatedAt":["Date"],"path.modules.231.createdAt":["Date"],"path.modules.231.updatedAt":["Date"],"path.modules.232.createdAt":["Date"],"path.modules.232.updatedAt":["Date"],"path.modules.233.createdAt":["Date"],"path.modules.233.updatedAt":["Date"],"path.modules.234.createdAt":["Date"],"path.modules.234.updatedAt":["Date"],"path.modules.235.createdAt":["Date"],"path.modules.235.updatedAt":["Date"],"path.modules.236.createdAt":["Date"],"path.modules.236.updatedAt":["Date"],"path.modules.237.createdAt":["Date"],"path.modules.237.updatedAt":["Date"],"path.modules.238.createdAt":["Date"],"path.modules.238.updatedAt":["Date"],"path.modules.239.createdAt":["Date"],"path.modules.239.updatedAt":["Date"],"path.modules.240.createdAt":["Date"],"path.modules.240.updatedAt":["Date"],"path.modules.241.createdAt":["Date"],"path.modules.241.updatedAt":["Date"],"path.modules.242.createdAt":["Date"],"path.modules.242.updatedAt":["Date"],"path.modules.243.createdAt":["Date"],"path.modules.243.updatedAt":["Date"],"path.modules.244.createdAt":["Date"],"path.modules.244.updatedAt":["Date"],"path.modules.245.createdAt":["Date"],"path.modules.245.updatedAt":["Date"],"path.modules.246.createdAt":["Date"],"path.modules.246.updatedAt":["Date"],"path.modules.247.createdAt":["Date"],"path.modules.247.updatedAt":["Date"],"path.modules.248.createdAt":["Date"],"path.modules.248.updatedAt":["Date"],"path.modules.249.createdAt":["Date"],"path.modules.249.updatedAt":["Date"],"path.modules.250.createdAt":["Date"],"path.modules.250.updatedAt":["Date"],"path.modules.251.createdAt":["Date"],"path.modules.251.updatedAt":["Date"],"path.modules.252.createdAt":["Date"],"path.modules.252.updatedAt":["Date"],"path.modules.253.createdAt":["Date"],"path.modules.253.updatedAt":["Date"],"path.modules.254.createdAt":["Date"],"path.modules.254.updatedAt":["Date"],"path.modules.255.createdAt":["Date"],"path.modules.255.updatedAt":["Date"],"path.modules.256.createdAt":["Date"],"path.modules.256.updatedAt":["Date"],"path.modules.257.createdAt":["Date"],"path.modules.257.updatedAt":["Date"],"path.modules.258.createdAt":["Date"],"path.modules.258.updatedAt":["Date"],"path.modules.259.createdAt":["Date"],"path.modules.259.updatedAt":["Date"],"path.modules.260.createdAt":["Date"],"path.modules.260.updatedAt":["Date"],"path.modules.261.createdAt":["Date"],"path.modules.261.updatedAt":["Date"],"path.modules.262.createdAt":["Date"],"path.modules.262.updatedAt":["Date"],"path.modules.263.createdAt":["Date"],"path.modules.263.updatedAt":["Date"],"path.modules.264.createdAt":["Date"],"path.modules.264.updatedAt":["Date"],"path.modules.265.createdAt":["Date"],"path.modules.265.updatedAt":["Date"],"path.modules.266.createdAt":["Date"],"path.modules.266.updatedAt":["Date"],"path.modules.267.createdAt":["Date"],"path.modules.267.updatedAt":["Date"],"path.modules.268.createdAt":["Date"],"path.modules.268.updatedAt":["Date"],"path.modules.269.createdAt":["Date"],"path.modules.269.updatedAt":["Date"],"path.modules.270.createdAt":["Date"],"path.modules.270.updatedAt":["Date"],"path.modules.271.createdAt":["Date"],"path.modules.271.updatedAt":["Date"],"path.modules.272.createdAt":["Date"],"path.modules.272.updatedAt":["Date"],"path.modules.273.createdAt":["Date"],"path.modules.273.updatedAt":["Date"],"path.modules.274.createdAt":["Date"],"path.modules.274.updatedAt":["Date"],"path.modules.275.createdAt":["Date"],"path.modules.275.updatedAt":["Date"],"path.modules.276.createdAt":["Date"],"path.modules.276.updatedAt":["Date"],"path.modules.277.createdAt":["Date"],"path.modules.277.updatedAt":["Date"],"path.modules.278.createdAt":["Date"],"path.modules.278.updatedAt":["Date"],"path.modules.279.createdAt":["Date"],"path.modules.279.updatedAt":["Date"],"path.modules.280.createdAt":["Date"],"path.modules.280.updatedAt":["Date"],"path.modules.281.createdAt":["Date"],"path.modules.281.updatedAt":["Date"],"path.modules.282.createdAt":["Date"],"path.modules.282.updatedAt":["Date"],"path.modules.283.createdAt":["Date"],"path.modules.283.updatedAt":["Date"],"path.modules.284.createdAt":["Date"],"path.modules.284.updatedAt":["Date"],"path.modules.285.createdAt":["Date"],"path.modules.285.updatedAt":["Date"],"path.modules.286.createdAt":["Date"],"path.modules.286.updatedAt":["Date"],"path.modules.287.createdAt":["Date"],"path.modules.287.updatedAt":["Date"],"path.modules.288.createdAt":["Date"],"path.modules.288.updatedAt":["Date"],"path.modules.289.createdAt":["Date"],"path.modules.289.updatedAt":["Date"],"path.modules.290.createdAt":["Date"],"path.modules.290.updatedAt":["Date"],"path.modules.291.createdAt":["Date"],"path.modules.291.updatedAt":["Date"],"path.modules.292.createdAt":["Date"],"path.modules.292.updatedAt":["Date"],"path.modules.293.createdAt":["Date"],"path.modules.293.updatedAt":["Date"],"path.modules.294.createdAt":["Date"],"path.modules.294.updatedAt":["Date"],"path.modules.295.createdAt":["Date"],"path.modules.295.updatedAt":["Date"],"path.modules.296.createdAt":["Date"],"path.modules.296.updatedAt":["Date"],"path.modules.297.createdAt":["Date"],"path.modules.297.updatedAt":["Date"],"path.modules.298.createdAt":["Date"],"path.modules.298.updatedAt":["Date"],"path.modules.299.createdAt":["Date"],"path.modules.299.updatedAt":["Date"],"path.modules.300.createdAt":["Date"],"path.modules.300.updatedAt":["Date"],"path.modules.301.createdAt":["Date"],"path.modules.301.updatedAt":["Date"],"path.modules.302.createdAt":["Date"],"path.modules.302.updatedAt":["Date"],"path.modules.303.createdAt":["Date"],"path.modules.303.updatedAt":["Date"],"path.modules.304.createdAt":["Date"],"path.modules.304.updatedAt":["Date"],"path.modules.305.createdAt":["Date"],"path.modules.305.updatedAt":["Date"],"path.modules.306.createdAt":["Date"],"path.modules.306.updatedAt":["Date"],"path.modules.307.createdAt":["Date"],"path.modules.307.updatedAt":["Date"],"path.modules.308.createdAt":["Date"],"path.modules.308.updatedAt":["Date"],"path.modules.309.createdAt":["Date"],"path.modules.309.updatedAt":["Date"],"path.modules.310.createdAt":["Date"],"path.modules.310.updatedAt":["Date"],"path.modules.311.createdAt":["Date"],"path.modules.311.updatedAt":["Date"],"path.modules.312.createdAt":["Date"],"path.modules.312.updatedAt":["Date"],"path.modules.313.createdAt":["Date"],"path.modules.313.updatedAt":["Date"],"path.modules.314.createdAt":["Date"],"path.modules.314.updatedAt":["Date"],"path.modules.315.createdAt":["Date"],"path.modules.315.updatedAt":["Date"],"path.modules.316.createdAt":["Date"],"path.modules.316.updatedAt":["Date"],"path.modules.317.createdAt":["Date"],"path.modules.317.updatedAt":["Date"],"path.modules.318.createdAt":["Date"],"path.modules.318.updatedAt":["Date"],"path.modules.319.createdAt":["Date"],"path.modules.319.updatedAt":["Date"],"path.modules.320.createdAt":["Date"],"path.modules.320.updatedAt":["Date"],"path.modules.321.createdAt":["Date"],"path.modules.321.updatedAt":["Date"],"path.modules.322.createdAt":["Date"],"path.modules.322.updatedAt":["Date"],"path.modules.323.createdAt":["Date"],"path.modules.323.updatedAt":["Date"],"path.modules.324.createdAt":["Date"],"path.modules.324.updatedAt":["Date"],"path.modules.325.createdAt":["Date"],"path.modules.325.updatedAt":["Date"],"path.modules.326.createdAt":["Date"],"path.modules.326.updatedAt":["Date"],"path.modules.327.createdAt":["Date"],"path.modules.327.updatedAt":["Date"],"currentModule.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.84.createdAt","currentModule.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.84.updatedAt","prevModule.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.83.createdAt","prevModule.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.83.updatedAt","nextModule.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.85.createdAt","nextModule.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.85.updatedAt","allModules.0.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.0.createdAt","allModules.0.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.0.updatedAt","allModules.1.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.1.createdAt","allModules.1.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.1.updatedAt","allModules.2.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.2.createdAt","allModules.2.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.2.updatedAt","allModules.3.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.3.createdAt","allModules.3.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.3.updatedAt","allModules.4.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.4.createdAt","allModules.4.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.4.updatedAt","allModules.5.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.5.createdAt","allModules.5.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.5.updatedAt","allModules.6.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.6.createdAt","allModules.6.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.6.updatedAt","allModules.7.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.7.createdAt","allModules.7.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.7.updatedAt","allModules.8.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.8.createdAt","allModules.8.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.8.updatedAt","allModules.9.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.9.createdAt","allModules.9.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.9.updatedAt","allModules.10.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.10.createdAt","allModules.10.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.10.updatedAt","allModules.11.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.11.createdAt","allModules.11.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.11.updatedAt","allModules.12.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.12.createdAt","allModules.12.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.12.updatedAt","allModules.13.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.13.createdAt","allModules.13.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.13.updatedAt","allModules.14.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.14.createdAt","allModules.14.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.14.updatedAt","allModules.15.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.15.createdAt","allModules.15.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.15.updatedAt","allModules.16.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.16.createdAt","allModules.16.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.16.updatedAt","allModules.17.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.17.createdAt","allModules.17.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.17.updatedAt","allModules.18.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.18.createdAt","allModules.18.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.18.updatedAt","allModules.19.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.19.createdAt","allModules.19.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.19.updatedAt","allModules.20.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.20.createdAt","allModules.20.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.20.updatedAt","allModules.21.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.21.createdAt","allModules.21.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.21.updatedAt","allModules.22.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.22.createdAt","allModules.22.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.22.updatedAt","allModules.23.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.23.createdAt","allModules.23.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.23.updatedAt","allModules.24.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.24.createdAt","allModules.24.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.24.updatedAt","allModules.25.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.25.createdAt","allModules.25.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.25.updatedAt","allModules.26.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.26.createdAt","allModules.26.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.26.updatedAt","allModules.27.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.27.createdAt","allModules.27.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.27.updatedAt","allModules.28.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.28.createdAt","allModules.28.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.28.updatedAt","allModules.29.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.29.createdAt","allModules.29.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.29.updatedAt","allModules.30.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.30.createdAt","allModules.30.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.30.updatedAt","allModules.31.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.31.createdAt","allModules.31.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.31.updatedAt","allModules.32.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.32.createdAt","allModules.32.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.32.updatedAt","allModules.33.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.33.createdAt","allModules.33.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.33.updatedAt","allModules.34.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.34.createdAt","allModules.34.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.34.updatedAt","allModules.35.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.35.createdAt","allModules.35.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.35.updatedAt","allModules.36.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.36.createdAt","allModules.36.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.36.updatedAt","allModules.37.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.37.createdAt","allModules.37.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.37.updatedAt","allModules.38.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.38.createdAt","allModules.38.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.38.updatedAt","allModules.39.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.39.createdAt","allModules.39.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.39.updatedAt","allModules.40.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.40.createdAt","allModules.40.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.40.updatedAt","allModules.41.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.41.createdAt","allModules.41.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.41.updatedAt","allModules.42.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.42.createdAt","allModules.42.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.42.updatedAt","allModules.43.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.43.createdAt","allModules.43.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.43.updatedAt","allModules.44.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.44.createdAt","allModules.44.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.44.updatedAt","allModules.45.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.45.createdAt","allModules.45.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.45.updatedAt","allModules.46.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.46.createdAt","allModules.46.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.46.updatedAt","allModules.47.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.47.createdAt","allModules.47.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.47.updatedAt","allModules.48.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.48.createdAt","allModules.48.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.48.updatedAt","allModules.49.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.49.createdAt","allModules.49.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.49.updatedAt","allModules.50.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.50.createdAt","allModules.50.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.50.updatedAt","allModules.51.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.51.createdAt","allModules.51.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.51.updatedAt","allModules.52.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.52.createdAt","allModules.52.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.52.updatedAt","allModules.53.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.53.createdAt","allModules.53.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.53.updatedAt","allModules.54.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.54.createdAt","allModules.54.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.54.updatedAt","allModules.55.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.55.createdAt","allModules.55.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.55.updatedAt","allModules.56.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.56.createdAt","allModules.56.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.56.updatedAt","allModules.57.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.57.createdAt","allModules.57.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.57.updatedAt","allModules.58.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.58.createdAt","allModules.58.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.58.updatedAt","allModules.59.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.59.createdAt","allModules.59.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.59.updatedAt","allModules.60.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.60.createdAt","allModules.60.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.60.updatedAt","allModules.61.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.61.createdAt","allModules.61.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.61.updatedAt","allModules.62.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.62.createdAt","allModules.62.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.62.updatedAt","allModules.63.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.63.createdAt","allModules.63.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.63.updatedAt","allModules.64.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.64.createdAt","allModules.64.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.64.updatedAt","allModules.65.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.65.createdAt","allModules.65.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.65.updatedAt","allModules.66.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.66.createdAt","allModules.66.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.66.updatedAt","allModules.67.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.67.createdAt","allModules.67.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.67.updatedAt","allModules.68.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.68.createdAt","allModules.68.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.68.updatedAt","allModules.69.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.69.createdAt","allModules.69.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.69.updatedAt","allModules.70.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.70.createdAt","allModules.70.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.70.updatedAt","allModules.71.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.71.createdAt","allModules.71.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.71.updatedAt","allModules.72.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.72.createdAt","allModules.72.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.72.updatedAt","allModules.73.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.73.createdAt","allModules.73.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.73.updatedAt","allModules.74.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.74.createdAt","allModules.74.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.74.updatedAt","allModules.75.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.75.createdAt","allModules.75.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.75.updatedAt","allModules.76.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.76.createdAt","allModules.76.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.76.updatedAt","allModules.77.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.77.createdAt","allModules.77.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.77.updatedAt","allModules.78.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.78.createdAt","allModules.78.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.78.updatedAt","allModules.79.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.79.createdAt","allModules.79.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.79.updatedAt","allModules.80.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.80.createdAt","allModules.80.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.80.updatedAt","allModules.81.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.81.createdAt","allModules.81.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.81.updatedAt","allModules.82.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.82.createdAt","allModules.82.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.82.updatedAt","allModules.83.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.83.createdAt","allModules.83.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.83.updatedAt","allModules.84.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.84.createdAt","allModules.84.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.84.updatedAt","allModules.85.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.85.createdAt","allModules.85.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.85.updatedAt","allModules.86.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.86.createdAt","allModules.86.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.86.updatedAt","allModules.87.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.87.createdAt","allModules.87.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.87.updatedAt","allModules.88.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.88.createdAt","allModules.88.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.88.updatedAt","allModules.89.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.89.createdAt","allModules.89.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.89.updatedAt","allModules.90.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.90.createdAt","allModules.90.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.90.updatedAt","allModules.91.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.91.createdAt","allModules.91.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.91.updatedAt","allModules.92.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.92.createdAt","allModules.92.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.92.updatedAt","allModules.93.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.93.createdAt","allModules.93.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.93.updatedAt","allModules.94.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.94.createdAt","allModules.94.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.94.updatedAt","allModules.95.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.95.createdAt","allModules.95.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.95.updatedAt","allModules.96.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.96.createdAt","allModules.96.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.96.updatedAt","allModules.97.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.97.createdAt","allModules.97.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.97.updatedAt","allModules.98.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.98.createdAt","allModules.98.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.98.updatedAt","allModules.99.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.99.createdAt","allModules.99.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.99.updatedAt","allModules.100.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.100.createdAt","allModules.100.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.100.updatedAt","allModules.101.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.101.createdAt","allModules.101.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.101.updatedAt","allModules.102.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.102.createdAt","allModules.102.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.102.updatedAt","allModules.103.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.103.createdAt","allModules.103.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.103.updatedAt","allModules.104.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.104.createdAt","allModules.104.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.104.updatedAt","allModules.105.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.105.createdAt","allModules.105.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.105.updatedAt","allModules.106.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.106.createdAt","allModules.106.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.106.updatedAt","allModules.107.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.107.createdAt","allModules.107.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.107.updatedAt","allModules.108.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.108.createdAt","allModules.108.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.108.updatedAt","allModules.109.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.109.createdAt","allModules.109.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.109.updatedAt","allModules.110.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.110.createdAt","allModules.110.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.110.updatedAt","allModules.111.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.111.createdAt","allModules.111.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.111.updatedAt","allModules.112.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.112.createdAt","allModules.112.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.112.updatedAt","allModules.113.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.113.createdAt","allModules.113.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.113.updatedAt","allModules.114.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.114.createdAt","allModules.114.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.114.updatedAt","allModules.115.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.115.createdAt","allModules.115.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.115.updatedAt","allModules.116.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.116.createdAt","allModules.116.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.116.updatedAt","allModules.117.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.117.createdAt","allModules.117.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.117.updatedAt","allModules.118.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.118.createdAt","allModules.118.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.118.updatedAt","allModules.119.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.119.createdAt","allModules.119.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.119.updatedAt","allModules.120.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.120.createdAt","allModules.120.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.120.updatedAt","allModules.121.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.121.createdAt","allModules.121.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.121.updatedAt","allModules.122.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.122.createdAt","allModules.122.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.122.updatedAt","allModules.123.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.123.createdAt","allModules.123.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.123.updatedAt","allModules.124.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.124.createdAt","allModules.124.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.124.updatedAt","allModules.125.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.125.createdAt","allModules.125.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.125.updatedAt","allModules.126.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.126.createdAt","allModules.126.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.126.updatedAt","allModules.127.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.127.createdAt","allModules.127.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.127.updatedAt","allModules.128.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.128.createdAt","allModules.128.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.128.updatedAt","allModules.129.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.129.createdAt","allModules.129.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.129.updatedAt","allModules.130.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.130.createdAt","allModules.130.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.130.updatedAt","allModules.131.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.131.createdAt","allModules.131.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.131.updatedAt","allModules.132.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.132.createdAt","allModules.132.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.132.updatedAt","allModules.133.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.133.createdAt","allModules.133.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.133.updatedAt","allModules.134.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.134.createdAt","allModules.134.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.134.updatedAt","allModules.135.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.135.createdAt","allModules.135.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.135.updatedAt","allModules.136.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.136.createdAt","allModules.136.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.136.updatedAt","allModules.137.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.137.createdAt","allModules.137.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.137.updatedAt","allModules.138.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.138.createdAt","allModules.138.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.138.updatedAt","allModules.139.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.139.createdAt","allModules.139.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.139.updatedAt","allModules.140.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.140.createdAt","allModules.140.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.140.updatedAt","allModules.141.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.141.createdAt","allModules.141.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.141.updatedAt","allModules.142.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.142.createdAt","allModules.142.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.142.updatedAt","allModules.143.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.143.createdAt","allModules.143.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.143.updatedAt","allModules.144.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.144.createdAt","allModules.144.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.144.updatedAt","allModules.145.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.145.createdAt","allModules.145.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.145.updatedAt","allModules.146.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.146.createdAt","allModules.146.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.146.updatedAt","allModules.147.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.147.createdAt","allModules.147.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.147.updatedAt","allModules.148.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.148.createdAt","allModules.148.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.148.updatedAt","allModules.149.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.149.createdAt","allModules.149.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.149.updatedAt","allModules.150.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.150.createdAt","allModules.150.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.150.updatedAt","allModules.151.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.151.createdAt","allModules.151.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.151.updatedAt","allModules.152.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.152.createdAt","allModules.152.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.152.updatedAt","allModules.153.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.153.createdAt","allModules.153.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.153.updatedAt","allModules.154.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.154.createdAt","allModules.154.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.154.updatedAt","allModules.155.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.155.createdAt","allModules.155.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.155.updatedAt","allModules.156.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.156.createdAt","allModules.156.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.156.updatedAt","allModules.157.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.157.createdAt","allModules.157.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.157.updatedAt","allModules.158.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.158.createdAt","allModules.158.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.158.updatedAt","allModules.159.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.159.createdAt","allModules.159.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.159.updatedAt","allModules.160.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.160.createdAt","allModules.160.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.160.updatedAt","allModules.161.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.161.createdAt","allModules.161.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.161.updatedAt","allModules.162.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.162.createdAt","allModules.162.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.162.updatedAt","allModules.163.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.163.createdAt","allModules.163.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.163.updatedAt","allModules.164.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.164.createdAt","allModules.164.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.164.updatedAt","allModules.165.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.165.createdAt","allModules.165.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.165.updatedAt","allModules.166.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.166.createdAt","allModules.166.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.166.updatedAt","allModules.167.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.167.createdAt","allModules.167.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.167.updatedAt","allModules.168.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.168.createdAt","allModules.168.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.168.updatedAt","allModules.169.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.169.createdAt","allModules.169.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.169.updatedAt","allModules.170.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.170.createdAt","allModules.170.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.170.updatedAt","allModules.171.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.171.createdAt","allModules.171.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.171.updatedAt","allModules.172.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.172.createdAt","allModules.172.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.172.updatedAt","allModules.173.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.173.createdAt","allModules.173.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.173.updatedAt","allModules.174.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.174.createdAt","allModules.174.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.174.updatedAt","allModules.175.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.175.createdAt","allModules.175.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.175.updatedAt","allModules.176.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.176.createdAt","allModules.176.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.176.updatedAt","allModules.177.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.177.createdAt","allModules.177.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.177.updatedAt","allModules.178.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.178.createdAt","allModules.178.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.178.updatedAt","allModules.179.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.179.createdAt","allModules.179.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.179.updatedAt","allModules.180.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.180.createdAt","allModules.180.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.180.updatedAt","allModules.181.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.181.createdAt","allModules.181.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.181.updatedAt","allModules.182.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.182.createdAt","allModules.182.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.182.updatedAt","allModules.183.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.183.createdAt","allModules.183.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.183.updatedAt","allModules.184.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.184.createdAt","allModules.184.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.184.updatedAt","allModules.185.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.185.createdAt","allModules.185.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.185.updatedAt","allModules.186.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.186.createdAt","allModules.186.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.186.updatedAt","allModules.187.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.187.createdAt","allModules.187.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.187.updatedAt","allModules.188.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.188.createdAt","allModules.188.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.188.updatedAt","allModules.189.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.189.createdAt","allModules.189.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.189.updatedAt","allModules.190.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.190.createdAt","allModules.190.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.190.updatedAt","allModules.191.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.191.createdAt","allModules.191.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.191.updatedAt","allModules.192.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.192.createdAt","allModules.192.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.192.updatedAt","allModules.193.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.193.createdAt","allModules.193.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.193.updatedAt","allModules.194.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.194.createdAt","allModules.194.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.194.updatedAt","allModules.195.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.195.createdAt","allModules.195.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.195.updatedAt","allModules.196.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.196.createdAt","allModules.196.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.196.updatedAt","allModules.197.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.197.createdAt","allModules.197.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.197.updatedAt","allModules.198.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.198.createdAt","allModules.198.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.198.updatedAt","allModules.199.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.199.createdAt","allModules.199.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.199.updatedAt","allModules.200.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.200.createdAt","allModules.200.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.200.updatedAt","allModules.201.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.201.createdAt","allModules.201.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.201.updatedAt","allModules.202.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.202.createdAt","allModules.202.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.202.updatedAt","allModules.203.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.203.createdAt","allModules.203.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.203.updatedAt","allModules.204.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.204.createdAt","allModules.204.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.204.updatedAt","allModules.205.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.205.createdAt","allModules.205.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.205.updatedAt","allModules.206.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.206.createdAt","allModules.206.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.206.updatedAt","allModules.207.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.207.createdAt","allModules.207.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.207.updatedAt","allModules.208.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.208.createdAt","allModules.208.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.208.updatedAt","allModules.209.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.209.createdAt","allModules.209.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.209.updatedAt","allModules.210.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.210.createdAt","allModules.210.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.210.updatedAt","allModules.211.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.211.createdAt","allModules.211.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.211.updatedAt","allModules.212.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.212.createdAt","allModules.212.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.212.updatedAt","allModules.213.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.213.createdAt","allModules.213.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.213.updatedAt","allModules.214.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.214.createdAt","allModules.214.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.214.updatedAt","allModules.215.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.215.createdAt","allModules.215.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.215.updatedAt","allModules.216.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.216.createdAt","allModules.216.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.216.updatedAt","allModules.217.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.217.createdAt","allModules.217.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.217.updatedAt","allModules.218.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.218.createdAt","allModules.218.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.218.updatedAt","allModules.219.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.219.createdAt","allModules.219.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.219.updatedAt","allModules.220.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.220.createdAt","allModules.220.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.220.updatedAt","allModules.221.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.221.createdAt","allModules.221.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.221.updatedAt","allModules.222.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.222.createdAt","allModules.222.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.222.updatedAt","allModules.223.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.223.createdAt","allModules.223.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.223.updatedAt","allModules.224.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.224.createdAt","allModules.224.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.224.updatedAt","allModules.225.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.225.createdAt","allModules.225.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.225.updatedAt","allModules.226.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.226.createdAt","allModules.226.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.226.updatedAt","allModules.227.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.227.createdAt","allModules.227.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.227.updatedAt","allModules.228.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.228.createdAt","allModules.228.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.228.updatedAt","allModules.229.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.229.createdAt","allModules.229.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.229.updatedAt","allModules.230.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.230.createdAt","allModules.230.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.230.updatedAt","allModules.231.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.231.createdAt","allModules.231.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.231.updatedAt","allModules.232.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.232.createdAt","allModules.232.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.232.updatedAt","allModules.233.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.233.createdAt","allModules.233.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.233.updatedAt","allModules.234.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.234.createdAt","allModules.234.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.234.updatedAt","allModules.235.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.235.createdAt","allModules.235.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.235.updatedAt","allModules.236.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.236.createdAt","allModules.236.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.236.updatedAt","allModules.237.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.237.createdAt","allModules.237.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.237.updatedAt","allModules.238.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.238.createdAt","allModules.238.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.238.updatedAt","allModules.239.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.239.createdAt","allModules.239.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.239.updatedAt","allModules.240.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.240.createdAt","allModules.240.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.240.updatedAt","allModules.241.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.241.createdAt","allModules.241.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.241.updatedAt","allModules.242.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.242.createdAt","allModules.242.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.242.updatedAt","allModules.243.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.243.createdAt","allModules.243.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.243.updatedAt","allModules.244.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.244.createdAt","allModules.244.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.244.updatedAt","allModules.245.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.245.createdAt","allModules.245.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.245.updatedAt","allModules.246.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.246.createdAt","allModules.246.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.246.updatedAt","allModules.247.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.247.createdAt","allModules.247.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.247.updatedAt","allModules.248.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.248.createdAt","allModules.248.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.248.updatedAt","allModules.249.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.249.createdAt","allModules.249.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.249.updatedAt","allModules.250.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.250.createdAt","allModules.250.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.250.updatedAt","allModules.251.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.251.createdAt","allModules.251.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.251.updatedAt","allModules.252.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.252.createdAt","allModules.252.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.252.updatedAt","allModules.253.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.253.createdAt","allModules.253.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.253.updatedAt","allModules.254.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.254.createdAt","allModules.254.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.254.updatedAt","allModules.255.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.255.createdAt","allModules.255.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.255.updatedAt","allModules.256.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.256.createdAt","allModules.256.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.256.updatedAt","allModules.257.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.257.createdAt","allModules.257.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.257.updatedAt","allModules.258.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.258.createdAt","allModules.258.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.258.updatedAt","allModules.259.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.259.createdAt","allModules.259.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.259.updatedAt","allModules.260.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.260.createdAt","allModules.260.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.260.updatedAt","allModules.261.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.261.createdAt","allModules.261.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.261.updatedAt","allModules.262.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.262.createdAt","allModules.262.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.262.updatedAt","allModules.263.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.263.createdAt","allModules.263.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.263.updatedAt","allModules.264.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.264.createdAt","allModules.264.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.264.updatedAt","allModules.265.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.265.createdAt","allModules.265.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.265.updatedAt","allModules.266.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.266.createdAt","allModules.266.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.266.updatedAt","allModules.267.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.267.createdAt","allModules.267.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.267.updatedAt","allModules.268.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.268.createdAt","allModules.268.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.268.updatedAt","allModules.269.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.269.createdAt","allModules.269.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.269.updatedAt","allModules.270.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.270.createdAt","allModules.270.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.270.updatedAt","allModules.271.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.271.createdAt","allModules.271.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.271.updatedAt","allModules.272.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.272.createdAt","allModules.272.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.272.updatedAt","allModules.273.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.273.createdAt","allModules.273.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.273.updatedAt","allModules.274.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.274.createdAt","allModules.274.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.274.updatedAt","allModules.275.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.275.createdAt","allModules.275.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.275.updatedAt","allModules.276.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.276.createdAt","allModules.276.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.276.updatedAt","allModules.277.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.277.createdAt","allModules.277.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.277.updatedAt","allModules.278.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.278.createdAt","allModules.278.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.278.updatedAt","allModules.279.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.279.createdAt","allModules.279.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.279.updatedAt","allModules.280.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.280.createdAt","allModules.280.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.280.updatedAt","allModules.281.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.281.createdAt","allModules.281.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.281.updatedAt","allModules.282.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.282.createdAt","allModules.282.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.282.updatedAt","allModules.283.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.283.createdAt","allModules.283.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.283.updatedAt","allModules.284.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.284.createdAt","allModules.284.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.284.updatedAt","allModules.285.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.285.createdAt","allModules.285.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.285.updatedAt","allModules.286.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.286.createdAt","allModules.286.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.286.updatedAt","allModules.287.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.287.createdAt","allModules.287.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.287.updatedAt","allModules.288.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.288.createdAt","allModules.288.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.288.updatedAt","allModules.289.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.289.createdAt","allModules.289.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.289.updatedAt","allModules.290.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.290.createdAt","allModules.290.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.290.updatedAt","allModules.291.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.291.createdAt","allModules.291.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.291.updatedAt","allModules.292.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.292.createdAt","allModules.292.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.292.updatedAt","allModules.293.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.293.createdAt","allModules.293.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.293.updatedAt","allModules.294.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.294.createdAt","allModules.294.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.294.updatedAt","allModules.295.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.295.createdAt","allModules.295.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.295.updatedAt","allModules.296.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.296.createdAt","allModules.296.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.296.updatedAt","allModules.297.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.297.createdAt","allModules.297.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.297.updatedAt","allModules.298.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.298.createdAt","allModules.298.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.298.updatedAt","allModules.299.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.299.createdAt","allModules.299.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.299.updatedAt","allModules.300.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.300.createdAt","allModules.300.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.300.updatedAt","allModules.301.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.301.createdAt","allModules.301.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.301.updatedAt","allModules.302.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.302.createdAt","allModules.302.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.302.updatedAt","allModules.303.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.303.createdAt","allModules.303.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.303.updatedAt","allModules.304.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.304.createdAt","allModules.304.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.304.updatedAt","allModules.305.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.305.createdAt","allModules.305.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.305.updatedAt","allModules.306.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.306.createdAt","allModules.306.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.306.updatedAt","allModules.307.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.307.createdAt","allModules.307.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.307.updatedAt","allModules.308.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.308.createdAt","allModules.308.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.308.updatedAt","allModules.309.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.309.createdAt","allModules.309.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.309.updatedAt","allModules.310.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.310.createdAt","allModules.310.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.310.updatedAt","allModules.311.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.311.createdAt","allModules.311.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.311.updatedAt","allModules.312.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.312.createdAt","allModules.312.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.312.updatedAt","allModules.313.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.313.createdAt","allModules.313.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.313.updatedAt","allModules.314.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.314.createdAt","allModules.314.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.314.updatedAt","allModules.315.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.315.createdAt","allModules.315.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.315.updatedAt","allModules.316.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.316.createdAt","allModules.316.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.316.updatedAt","allModules.317.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.317.createdAt","allModules.317.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.317.updatedAt","allModules.318.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.318.createdAt","allModules.318.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.318.updatedAt","allModules.319.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.319.createdAt","allModules.319.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.319.updatedAt","allModules.320.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.320.createdAt","allModules.320.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.320.updatedAt","allModules.321.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.321.createdAt","allModules.321.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.321.updatedAt","allModules.322.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.322.createdAt","allModules.322.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.322.updatedAt","allModules.323.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.323.createdAt","allModules.323.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.323.updatedAt","allModules.324.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.324.createdAt","allModules.324.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.324.updatedAt","allModules.325.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.325.createdAt","allModules.325.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.325.updatedAt","allModules.326.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.326.createdAt","allModules.326.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.326.updatedAt","allModules.327.createdAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.327.createdAt","allModules.327.updatedAt":"$45:props:state:queries:1:state:data:meta:values:path.modules.327.updatedAt"},"referentialEqualities":{"allModules":["path.modules"],"prevModule":["path.modules.83"],"currentModule":["path.modules.84"],"nextModule":["path.modules.85"]},"v":1}},"dataUpdateCount":1,"dataUpdatedAt":1792296755841,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningPaths","getModule"],{"input":{"pathSlug":"machine-learning","contentType":"LEARNING","contentSlug":"regression-trees"},"type":"query"}],"queryHash":"[[\"learningPaths\",\"getModule\"],{\"input\":{\"contentSlug\":\"regression-trees\",\"contentType\":\"LEARNING\",\"pathSlug\":\"machine-learning\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"state":{"data":{"json":{"id":"seed_75ac796c0baf8a27d0650dac6c1c39bf","slug":"regression-trees","category":"Machine Learning","topic":"Decision Trees","title":"Regression Trees","description":"Master regression trees from CART algorithm fundamentals through splitting criteria, leaf predictions, piecewise constant approximation theory, and comprehensive comparisons with linear models. Develop deep understanding of how trees partition continuous target spaces.","difficulty":"INTERMEDIATE","estimatedMinutes":90,"pages":[{"id":"seed_4ac677a4cffa4eb4b8e7494abddfe864","title":"CART for Regression","order":0,"content":[{"id":"opening-hook","title":"The Algorithm That Changed Regression Trees","content":[{"data":{"content":"In 1984, Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone published their seminal book *Classification and Regression Trees* (CART), introducing what would become the most influential decision tree algorithm in machine learning history. While decision trees had existed before CART, this algorithm brought a level of mathematical rigor, computational efficiency, and practical elegance that transformed trees from a heuristic technique into a principled statistical methodology.\n\n**CART for regression** addresses a fundamental question: *How can we recursively partition a feature space to predict a continuous target variable?* The answer lies in a beautifully simple yet powerful algorithm that has stood the test of time for four decades and remains the foundation of modern ensemble methods like Random Forests and Gradient Boosting."},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will understand the complete CART regression algorithm: how it recursively partitions feature space, why binary splits are both necessary and sufficient, how optimal split points are computed, and the mathematical principles that govern its behavior. You will be equipped to implement CART from scratch and understand its role as the foundation of modern tree-based methods.","variant":"info"},"type":"callout"}]},{"id":"historical-context","title":"Historical Context and Motivation","content":[{"data":{"content":"$71"},"type":"text"},{"data":{"icon":"Info","title":"Why 'CART' Specifically?","content":"The name CART reflects that the same algorithmic framework handles both Classification (categorical targets) and Regression (continuous targets). The key differences lie in the splitting criterion (Gini/entropy for classification, MSE for regression) and the leaf predictions (class probabilities vs. mean values). This unified framework was a key insight of the original work.","variant":"note"},"type":"callout"},{"data":{"content":"**The mathematical philosophy of CART:**\n\nCART operates on a deceptively simple principle: at each step, find the single binary split that most reduces prediction error. By composing many such locally optimal splits, the algorithm constructs a globally effective predictor. This greedy, recursive approach sacrifices global optimality (finding the best tree is NP-complete) for computational tractability while achieving remarkably good practical performance.\n\nThe algorithm's beauty lies in this balance: it is simple enough to understand completely, efficient enough to scale to large datasets, and flexible enough to capture complex patterns that would elude parametric methods."},"type":"text"}],"titleType":"number","titleNumber":1},{"id":"cart-algorithm","title":"The CART Regression Algorithm","content":[{"data":{"content":"The CART algorithm for regression is a recursive procedure that builds a binary tree by repeatedly splitting the feature space. Let's formalize this precisely.\n\n**Problem Setup:**\n\nGiven training data $\\{(\\mathbf{x}_i, y_i)\\}_{i=1}^{n}$ where:\n- $\\mathbf{x}_i \\in \\mathbb{R}^p$ is the feature vector for observation $i$\n- $y_i \\in \\mathbb{R}$ is the continuous target value\n- We seek a function $\\hat{f}(\\mathbf{x})$ that minimizes prediction error\n\n**Core Algorithm (Recursive Partitioning):**\n\nThe algorithm maintains a partition of the feature space into disjoint regions $\\{R_1, R_2, \\ldots, R_M\\}$ where $M$ is the number of terminal nodes (leaves). In each region $R_m$, the prediction is constant:\n\n$$\\hat{f}(\\mathbf{x}) = \\sum_{m=1}^{M} c_m \\cdot \\mathbb{1}(\\mathbf{x} \\in R_m)$$\n\nwhere $c_m$ is the predicted value for region $R_m$ and $\\mathbb{1}(\\cdot)$ is the indicator function."},"type":"text"},{"data":{"fileName":"cart_regression_algorithm.py","languages":[{"code":"$72","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"content":"**Understanding the recursion:**\n\nThe algorithm above embodies the divide-and-conquer strategy that defines CART:\n\n1. **Base case evaluation**: Check if stopping criteria are met (depth, sample size, purity)\n2. **Split search**: If not stopping, find the optimal binary split across all features\n3. **Partition**: Divide samples into left and right child nodes based on the split\n4. **Recurse**: Apply the same procedure to each child node\n5. **Construct**: Build the tree by connecting nodes through parent-child relationships\n\nThis recursive structure naturally produces a binary tree where:\n- **Internal nodes** represent splitting decisions (feature and threshold)\n- **Leaf nodes** represent predictions (mean of samples reaching that region)\n- **Edges** represent the outcomes of split conditions (≤ or >)"},"type":"text"}],"titleType":"number","titleNumber":2},{"id":"optimal-split","title":"Finding the Optimal Split","content":[{"data":{"content":"$73"},"type":"text"},{"data":{"fileName":"find_best_split.py","languages":[{"code":"$74","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Zap","title":"Computational Complexity","content":"The naive split search is O(n²p) per node—for each of p features, we consider O(n) thresholds, and for each threshold, we compute statistics in O(n). The efficient version reduces this to O(np log n) by sorting features once (O(n log n) each) and using O(n) incremental scans. For large n, this optimization is crucial.","variant":"tip"},"type":"callout"},{"data":{"content":"**Why variance minimization?**\n\nThe choice of variance (or equivalently, MSE) as the splitting criterion for regression is not arbitrary—it corresponds to maximum likelihood estimation under a Gaussian noise model:\n\n$$y_i = f(\\mathbf{x}_i) + \\epsilon_i, \\quad \\epsilon_i \\sim \\mathcal{N}(0, \\sigma^2)$$\n\nUnder this model, minimizing $\\sum_i (y_i - \\hat{y}_i)^2$ is equivalent to maximizing likelihood. The mean prediction in each region is the maximum likelihood estimate of the true function value for that region.\n\n**Connection to analysis of variance (ANOVA):**\n\nThe split search can also be viewed through the lens of one-way ANOVA. We're essentially asking: \"Which binary grouping of samples (defined by feature $j$ and threshold $t$) explains the most variance in $y$?\" The information gain (variance reduction) is proportional to the between-group sum of squares in an ANOVA decomposition."},"type":"text"}],"titleType":"number","titleNumber":3},{"id":"binary-splits","title":"Binary Splits: Why and How","content":[{"data":{"content":"$75"},"type":"text"},{"data":{"rows":[["**Continuous features**","O(n) threshold candidates","Must discretize or use exponential search"],["**Categorical features**","2^(k-1)-1 subsets for k categories","Could use k-way split but loses hierarchy"],["**Tree depth**","May be deeper for same expressiveness","Shallower but wider"],["**Interpretability**","Each decision is a simple yes/no","More complex per-node decisions"],["**Computational cost**","O(np log n) per level","Generally exponential in categories"],["**Expressiveness**","Complete (can represent any partition)","More direct for some patterns"]],"caption":"Comparison: Binary vs. Multi-way Splits","headers":["Aspect","Binary Splits","Multi-way Splits"]},"type":"table"},{"data":{"content":"**Handling categorical features:**\n\nFor categorical features with $k$ levels, CART considers all possible binary partitions into \"left\" and \"right\" subsets. There are $2^{k-1} - 1$ such partitions (excluding empty sets and the trivial all-in-one partition).\n\nFor regression, a remarkable optimization exists: **sorting categories by their mean target value** and only considering splits that separate contiguous groups in this ordering is guaranteed to find the optimal partition. This reduces complexity from $O(2^k)$ to $O(k \\log k)$.\n\n**Proof sketch**: For regression with MSE criterion, the optimal split always places categories with similar mean targets on the same side. This is because mixing categories with high and low means increases within-group variance."},"type":"text"},{"data":{"fileName":"categorical_split.py","languages":[{"code":"$76","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"}],"titleType":"number","titleNumber":4},{"id":"prediction","title":"Prediction and Tree Traversal","content":[{"data":{"content":"Once the tree is built, prediction is straightforward: for a new observation $\\mathbf{x}$, we traverse from root to leaf by following the appropriate branch at each internal node, then return the leaf's prediction.\n\n**Prediction algorithm:**\n\n$$\\hat{y} = \\text{predict}(\\mathbf{x}) = \\text{traverse}(\\text{root}, \\mathbf{x})$$\n\nwhere traverse recursively follows splits:\n- At internal node: if $x_j \\leq t$, go left; else go right\n- At leaf node: return the stored prediction (mean of training targets)\n\n**Computational complexity:**\n\nTraversal visits at most $d$ nodes where $d$ is tree depth, making prediction $O(d)$. For a balanced tree with $n$ training samples, $d = O(\\log n)$. However, unbalanced trees can have $d = O(n)$ in degenerate cases."},"type":"text"},{"data":{"fileName":"tree_prediction.py","languages":[{"code":"$77","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Info","title":"Prediction as Region Identification","content":"Each prediction path uniquely identifies a rectangular region in feature space. The prediction value is constant throughout this region. This property—that predictions are piecewise constant over axis-aligned boxes—is fundamental to understanding regression tree behavior and limitations.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"stopping-criteria","title":"Stopping Criteria and Tree Control","content":[{"data":{"content":"Without stopping criteria, CART would grow until every leaf contains a single training sample (or all targets in a leaf are identical). This would perfectly fit the training data but generalize poorly—a classic case of overfitting.\n\n**Common stopping criteria:**\n\n1. **Maximum depth** (`max_depth`): Limits tree height\n2. **Minimum samples to split** (`min_samples_split`): Requires minimum samples before considering a split\n3. **Minimum samples per leaf** (`min_samples_leaf`): Ensures each leaf has sufficient support\n4. **Minimum impurity decrease** (`min_impurity_decrease`): Requires splits to improve by threshold\n5. **Maximum leaf nodes** (`max_leaf_nodes`): Caps total leaves (grows best-first rather than depth-first)\n\n**Trade-offs:**\n\nEach criterion balances bias and variance differently:\n- Aggressive stopping (small `max_depth`, large `min_samples_leaf`) → High bias, low variance\n- Permissive stopping (large `max_depth`, small `min_samples_leaf`) → Low bias, high variance"},"type":"text"},{"data":{"left":[{"data":{"items":["Training error near zero, high test error","Many leaves with single samples","Extremely deep trees","Large discrepancy in train/test performance","High sensitivity to small data changes"],"title":{"icon":"AlertCircle","text":"Signs of Overfitting","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Similar train and test error","Leaves with reasonable sample counts","Interpretable tree structure","Stable performance across cross-validation folds","Predictions vary smoothly with inputs"],"title":{"icon":"CheckCircle","text":"Signs of Good Fit","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"fileName":"stopping_criteria.py","languages":[{"code":"$78","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"}],"titleType":"number","titleNumber":6},{"id":"complete-implementation","title":"Complete CART Implementation","content":[{"data":{"content":"Let's bring everything together into a complete, production-quality CART regression implementation that includes all the components we've discussed."},"type":"text"},{"data":{"fileName":"complete_cart.py","languages":[{"code":"$79","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"}],"titleType":"number","titleNumber":7},{"id":"cart-in-practice","title":"CART in Modern Practice","content":[{"data":{"content":"While CART forms the algorithmic foundation, modern practice involves additional considerations for robust deployment.\n\n**Using scikit-learn's DecisionTreeRegressor:**\n\nIn production, you'll typically use established libraries rather than custom implementations. scikit-learn's `DecisionTreeRegressor` implements optimized CART with additional features:"},"type":"text"},{"data":{"fileName":"sklearn_cart.py","languages":[{"code":"$7a","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Lightbulb","title":"When to Use CART for Regression","content":"Single decision trees excel when: (1) Interpretability is critical—you need to explain decisions; (2) Data has natural hierarchical/interaction structure; (3) You're building base learners for ensembles; (4) Features are mixed types (numeric and categorical); (5) Quick baseline models are needed. For maximum predictive power, trees are typically used within Random Forest or Gradient Boosting ensembles.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":8},{"id":"summary","title":"Summary: CART for Regression","content":[{"data":{"content":"We've completed a comprehensive examination of the CART algorithm for regression—the foundational method for constructing regression decision trees."},"type":"text"},{"data":{"type":"ordered","items":["**CART uses greedy recursive partitioning** — At each node, find the single binary split that most reduces MSE, then recurse on child nodes.","**Binary splits are both sufficient and efficient** — Any partition can be expressed through binary splits; searching is polynomial rather than exponential.","**Optimal splits minimize weighted child variance** — This corresponds to maximum likelihood under Gaussian noise assumptions.","**Efficient algorithms exploit sorted scans** — O(np log n) per level rather than naive O(n²p) through incremental statistics.","**Stopping criteria control overfitting** — max_depth, min_samples_split, min_samples_leaf balance bias and variance.","**Predictions are piecewise constant** — Each leaf defines a rectangular region with constant prediction value.","**CART forms the foundation for ensembles** — Random Forests and Gradient Boosting compose many CART trees for improved performance."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's next:**\n\nWith the CART algorithm established, the next page examines the **splitting criterion** in depth—specifically, why Mean Squared Error (MSE) is used for regression and how it connects to broader statistical principles."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now have a deep understanding of the CART regression algorithm—its mathematical foundations, computational implementation, and practical considerations. This knowledge is essential for understanding how modern tree-based methods like Random Forests and XGBoost work at their core.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":9}],"isLocked":false},{"id":"seed_5c865b327c14cb2ba7d85d96e8a7d6ff","title":"Splitting Criterion (MSE)","order":1,"content":null,"isLocked":true},{"id":"seed_c7dd375c88d35491e1c784ea8844cfcf","title":"Leaf Predictions","order":2,"content":null,"isLocked":true},{"id":"seed_b042f5d3ad71aedacdc24b113c501dbe","title":"Piecewise Constant Approximation","order":3,"content":null,"isLocked":true},{"id":"seed_9443337f0d6c814c51135b3ae8d7e3be","title":"Comparison to Linear Models","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792296755944,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"regression-trees"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"regression-trees\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296755945,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_75ac796c0baf8a27d0650dac6c1c39bf"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_75ac796c0baf8a27d0650dac6c1c39bf\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296755945,"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_9443337f0d6c814c51135b3ae8d7e3be"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_9443337f0d6c814c51135b3ae8d7e3be\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792296755945,"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_9443337f0d6c814c51135b3ae8d7e3be"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_9443337f0d6c814c51135b3ae8d7e3be\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792296755945,"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_9443337f0d6c814c51135b3ae8d7e3be"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_9443337f0d6c814c51135b3ae8d7e3be\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792296755974,"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_9443337f0d6c814c51135b3ae8d7e3be","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_9443337f0d6c814c51135b3ae8d7e3be\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@7b"}]},"children":"$L7c"}]