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Concepts5

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

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🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginner
📚TheoryIntermediate

Classifier-Free Guidance

Classifier-Free Guidance (CFG) steers diffusion sampling toward a condition (like a text prompt) without needing a separate classifier.

#classifier-free guidance#diffusion models#epsilon prediction+11
📚TheoryIntermediate

Flow Matching

Flow matching learns a time-dependent vector field v_t(x, c) whose ODE transports simple noise to complex data, enabling fast, deterministic sampling.

#flow matching
Intermediate
Advanced
Group:
Generative Model Theory
#conditional flow matching
#rectified flow
+11
📚TheoryIntermediate

Autoregressive Models

Autoregressive (AR) models represent a joint distribution by multiplying conditional probabilities in a fixed order, using the chain rule of probability.

#autoregressive#ar model#n-gram+11
∑MathIntermediate

Wasserstein Distance & Optimal Transport

Wasserstein distance (Earth Mover’s Distance) measures how much “work” is needed to transform one probability distribution into another by moving mass with minimal total cost.

#wasserstein distance#earth mover's distance#optimal transport+12
📚TheoryIntermediate

Maximum Likelihood & Generative Models

Maximum Likelihood Estimation (MLE) picks parameters that make the observed data most probable under a chosen probabilistic model.

#maximum likelihood#generative models#naive bayes+12