Xavier/Glorot Initialization
Understand Xavier (Glorot) initialization, the weight initialization technique that maintains signal variance across layers for stable deep network training.
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ML Concepts - Page 15
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
158
Total Concepts
249
Topics
Page 15
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Concepts - Page 15
Contrastive Loss Functions
Master contrastive loss functions including InfoNCE, NT-Xent, and Triplet Loss for representation learning and self-supervised training.
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Focal Loss for Imbalanced Classification
Master focal loss, the game-changing loss function that addresses extreme class imbalance by down-weighting easy examples and focusing on hard negatives.
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KL Divergence
Understand Kullback-Leibler divergence, the fundamental measure of difference between probability distributions used in VAEs, information theory, and model compression.
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MSE and MAE Loss Functions
Understand Mean Squared Error (MSE) and Mean Absolute Error (MAE), the fundamental loss functions for regression tasks with different sensitivity to outliers.
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SoA vs AoS: Data Layout Optimization
Master Structure of Arrays (SoA) vs Array of Structures (AoS) data layouts for optimal cache efficiency, SIMD vectorization, and GPU memory coalescing with interactive visualizations.
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Showing 85-90 of 158 articles