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Concepts152

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

AllBeginnerIntermediateAdvanced
📚TheoryAdvanced

Diffusion Models Theory

Diffusion models learn to reverse a simple noising process by estimating the score (the gradient of the log density) of data at different noise levels.

#diffusion models#ddpm#score matching+12
📚TheoryIntermediate

NP-Completeness

NP-completeness classifies decision problems that are both in NP and as hard as any problem in NP via polynomial-time reductions.

#np-complete
89101112
#np-hard
#polynomial-time reduction
+12
📚TheoryAdvanced

Variational Inference Theory

Variational Inference (VI) replaces an intractable posterior with a simpler distribution and optimizes it by minimizing KL divergence, which is equivalent to maximizing the ELBO.

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

ELBO (Evidence Lower Bound)

The Evidence Lower Bound (ELBO) is a tractable lower bound on the log evidence log p(x) that enables learning and inference in latent variable models like VAEs.

#elbo#variational inference#vae+12
📚TheoryAdvanced

P vs NP Problem

P vs NP asks whether every problem whose solutions can be verified quickly can also be solved quickly.

#p vs np#np-complete#np-hard+12
📚TheoryAdvanced

GAN Theory

Generative Adversarial Networks (GANs) set up a two-player game where a generator tries to make fake samples that look real while a discriminator tries to tell real from fake.

#gan minimax#wasserstein gan#js divergence+11
📚TheoryIntermediate

Complexity Theory

Complexity theory classifies problems by the resources required to solve or verify them, such as time and memory.

#complexity theory#p vs np#np-complete+12
📚TheoryAdvanced

Representation Learning Theory

Representation learning aims to automatically discover features that make downstream tasks easy, often without human-provided labels.

#representation learning#contrastive learning#infonce+12
📚TheoryAdvanced

Information Bottleneck Theory

Information Bottleneck (IB) studies how to compress an input X into a representation Z that still preserves what is needed to predict Y.

#information bottleneck#mutual information#variational information bottleneck+12
📚TheoryIntermediate

Contrastive Learning Theory

Contrastive learning learns representations by pulling together positive pairs and pushing apart negatives using a softmax-based objective.

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

Policy Gradient Theorem

The policy gradient theorem tells us how to push a stochastic policy’s parameters to increase expected return by following the gradient of expected rewards.

#policy gradient#reinforce#actor-critic+11
📚TheoryIntermediate

Bellman Equations

Bellman equations express how the value of a state or action equals immediate reward plus discounted value of what follows.

#bellman equation#value iteration#policy iteration+12