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Concepts57

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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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🔷All∑Math⚙️Algo🗂️DS📚Theory

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📚TheoryAdvanced

Streaming Algorithm Theory

Streaming algorithms process massive data one pass at a time using sublinear—often polylogarithmic—memory.

#streaming algorithms#count-min sketch#misra-gries+12
📚TheoryAdvanced

Distributed Algorithm Theory

Distributed algorithm theory studies how many independent computers cooperate correctly and efficiently despite delays and failures.

#distributed algorithms
12345
Advanced
#message passing
#synchronous rounds
+12
📚TheoryAdvanced

Algorithmic Information Theory

Algorithmic Information Theory studies information content via the shortest programs that generate data, rather than via average-case probabilities.

#kolmogorov complexity#algorithmic probability#solomonoff induction+11
📚TheoryAdvanced

Optimal Transport Theory

Optimal Transport (OT) formalizes the cheapest way to move one probability distribution into another given a cost to move mass.

#optimal transport#wasserstein distance#kantorovich+12
📚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
📚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
📚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
📚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
📚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
📚TheoryAdvanced

Transformer Theory

Transformers map sequences to sequences using layers of self-attention and feed-forward networks wrapped with residual connections and LayerNorm.

#transformer#self-attention#positional encoding+12