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Concepts95

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📐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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AllBeginnerIntermediate
📚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
📚TheoryIntermediate

Mean Field Variational Family

Mean field variational family assumes the joint posterior over latent variables factorizes into independent pieces q(z) = ∏ q_i(z_i).

#mean field
12345
Advanced
#variational inference
#elbo
+11
📚TheoryIntermediate

Variational Inference

Variational Inference (VI) turns Bayesian inference into an optimization problem by choosing a simple family q(z) to approximate an intractable posterior p(z|x).

#variational inference#elbo#kl divergence+12
📚TheoryIntermediate

RLHF Mathematics

RLHF turns human preferences between two model outputs into training signals using a probabilistic model of choice.

#rlhf#bradley-terry#pairwise comparisons+11
📚TheoryIntermediate

Exploration-Exploitation Tradeoff

The exploration–exploitation tradeoff is the tension between trying new actions to learn (exploration) and using the best-known action to earn rewards now (exploitation).

#multi-armed bandit#exploration exploitation#ucb1+12
📚TheoryIntermediate

Value Function Approximation

Value function approximation replaces a huge table of values with a small set of parameters that can generalize across similar states.

#reinforcement learning#value function approximation#linear function approximator+12
📚TheoryIntermediate

Metric Learning

Metric learning is about automatically learning a distance function so that similar items are close and dissimilar items are far in a feature space.

#metric learning#mahalanobis distance#contrastive loss+12
📚TheoryIntermediate

Self-Supervised Learning Theory

Self-supervised learning (SSL) teaches models to learn useful representations from unlabeled data by solving proxy tasks created directly from the data.

#self-supervised learning#contrastive learning#infonce+12
📚TheoryIntermediate

Contrastive Learning

Contrastive learning teaches models by pulling together similar examples (positives) and pushing apart dissimilar ones (negatives).

#contrastive learning#infonce#nt-xent+12
📚TheoryIntermediate

Embedding Spaces & Distributed Representations

Embedding spaces map discrete things like words or products to dense vectors so that similar items are close together.

#embeddings#dense vectors#cosine similarity+12
📚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#conditional flow matching#rectified flow+11