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How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts172

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

AllBeginnerIntermediate
📚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
678910
Advanced
#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
📚TheoryAdvanced

Calculus of Variations

Calculus of variations optimizes functionals—numbers produced by whole functions—rather than ordinary functions of numbers.

#calculus of variations#euler–lagrange#functional derivative+12
📚TheoryAdvanced

Deep Learning Generalization Theory

Deep learning generalization theory tries to explain why overparameterized networks can fit (interpolate) training data yet still perform well on new data.

#generalization#implicit regularization#minimum norm+12
📚TheoryAdvanced

Neural Network Expressivity

Neural network expressivity studies what kinds of functions different network architectures can represent and how efficiently they can do so.

#neural network expressivity#depth separation#relu linear regions+12
📚TheoryAdvanced

Statistical Learning Theory

Statistical learning theory explains why a model that fits training data can still predict well on unseen data by relating true risk to empirical risk plus a complexity term.

#statistical learning theory#empirical risk minimization#structural risk minimization+11
📚TheoryAdvanced

VC Dimension

VC dimension measures how many distinct labelings a hypothesis class can realize on any set of points of a given size.

#vc dimension#vapnik chervonenkis#shattering+12
📚TheoryAdvanced

Rademacher Complexity

Rademacher complexity is a data-dependent measure of how well a function class can fit random noise on a given sample.

#rademacher complexity#empirical rademacher#generalization bounds+12
📚TheoryAdvanced

Measure Theory

Measure theory generalizes length, area, and probability to very flexible spaces while keeping countable additivity intact.

#measure theory#sigma-algebra#lebesgue integral+12
⚙️AlgorithmAdvanced

DP on Broken Profile - Plug DP

Plug DP (DP on broken profile with plugs) sweeps a grid cell by cell while remembering how partial path segments cross the frontier as labeled “plugs.”

#plug dp#broken profile#hamiltonian path+12
⚙️AlgorithmAdvanced

Matrix Exponentiation - Advanced

Matrix exponentiation turns repeated linear transitions into fast O(n^{3} log k) computation using exponentiation by squaring.

#matrix exponentiation#adjacency matrix#walk counting+12