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How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts20

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

AllBeginnerIntermediate
∑MathIntermediate

Rényi Entropy & Divergence

Rényi entropy generalizes Shannon entropy by measuring uncertainty with a tunable emphasis on common versus rare outcomes.

#renyi entropy#renyi divergence#shannon entropy+12
⚙️AlgorithmIntermediate

Mixed Precision Training

Mixed precision training stores and computes tensors in low precision (FP16/BF16) for speed and memory savings while keeping a master copy of weights in FP32 for accurate updates.

#mixed precision
12
Advanced
Filtering by:
#numerical stability
#fp16
#bf16
+10
∑MathIntermediate

Orthogonal & Unitary Matrices

Orthogonal (real) and unitary (complex) matrices are length- and angle-preserving transformations, like perfect rotations and reflections.

#orthogonal matrix#unitary matrix#conjugate transpose+12
∑MathIntermediate

State Space Models (SSM)

A State Space Model (SSM) describes a dynamical system using a state vector x(t) that evolves via a first-order matrix differential equation and produces outputs y(t).

#state space#matrix exponential#controllability+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#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
∑MathIntermediate

Softmax & Temperature Scaling

Softmax turns arbitrary real-valued scores (logits) into probabilities that sum to one.

#softmax#temperature scaling#logits+12
📚TheoryIntermediate

Multi-Head Attention

Multi-Head Attention runs several attention mechanisms in parallel so each head can focus on different relationships in the data.

#multi-head attention#scaled dot-product attention#transformer+12
📚TheoryIntermediate

Scaled Dot-Product Attention

Scaled dot-product attention scores how much each value V should contribute to a query by taking dot products with keys K, scaling by \(\sqrt{d_k}\), applying softmax, and forming a weighted sum.

#scaled dot-product attention#softmax#transformer+10
📚TheoryIntermediate

Label Smoothing

Label smoothing replaces a hard one-hot target with a slightly softened distribution to reduce model overconfidence.

#label smoothing#cross-entropy#softmax+12
📚TheoryIntermediate

Layer Normalization

Layer Normalization rescales and recenters each sample across its feature dimensions, making it independent of batch size.

#layer normalization#gamma beta#feature normalization+12
⚙️AlgorithmIntermediate

Matrix Factorizations (Numerical)

Matrix factorizations rewrite a matrix into simpler building blocks (triangular or orthogonal) that make solving and analyzing linear systems much easier.

#lu decomposition#qr factorization#householder reflections+12