🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
📝Daily Log🎯Prompts🧠Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts3

Groups

📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginner
📚TheoryIntermediate

Knowledge Distillation Loss

Knowledge distillation loss blends standard hard-label cross-entropy with a soft distribution match from a teacher using a temperature parameter.

#knowledge distillation#kd loss#temperature scaling+12
📚TheoryIntermediate

Focal Loss

Focal Loss reshapes cross-entropy so that hard, misclassified examples get more focus while easy, well-classified ones are down-weighted.

#focal loss
Intermediate
Advanced
Filtering by:
#softmax
Group:
Loss Functions & Objectives
#class imbalance
#cross-entropy
+11
∑MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy#binary cross-entropy#softmax+11