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Concepts532

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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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AllBeginnerIntermediateAdvanced
📚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
2223242526
#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
📚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
📚TheoryAdvanced

Reinforcement Learning Theory

Reinforcement Learning (RL) studies how an agent learns to act in an environment to maximize long-term cumulative reward.

#reinforcement learning#mdp#bellman equation+12
📚TheoryAdvanced

Neural Tangent Kernel (NTK) Theory

The Neural Tangent Kernel (NTK) connects very wide neural networks to classical kernel methods, letting us study training as if it were kernel regression.

#neural tangent kernel#ntk#infinite width+12
📚TheoryIntermediate

Attention Mechanism Theory

Attention computes a weighted sum of values V where the weights come from how similar queries Q are to keys K.

#attention#self-attention#multi-head attention+12