🎓How I Study AIHISA

📖Read

📄Papers 📰Blogs 🎬Courses

💡Learn

🛤️Paths 📚Topics 💡Concepts 🎴Shorts

🎯Practice

📝Daily Log 🎯Prompts 🧠Review

Search Settings

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

Concepts95

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

All Beginner Intermediate

📚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

📚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.

#self-attention

#multi-head attention

+12

📚TheoryIntermediate

Scaling Laws

Scaling laws say that model loss typically follows a power law that improves predictably as you increase parameters, data, or compute.

#scaling laws#power law#chinchilla scaling+12

📚TheoryIntermediate

Universal Approximation Theorem

The Universal Approximation Theorem (UAT) says a feedforward neural network with one hidden layer and a non-polynomial activation (like sigmoid or ReLU) can approximate any continuous function on a compact set as closely as we want.

#universal approximation theorem#cybenko#hornik+12

📚TheoryIntermediate

Minimax Theorem

The Minimax Theorem states that in zero-sum two-player games with suitable convexity and compactness, the best guaranteed payoff for the maximizer equals the worst-case loss for the minimizer.

#minimax theorem#zero-sum games#saddle point+12

📚TheoryIntermediate

PAC Learning

PAC learning formalizes when a learner can probably (with probability at least 1−δ) and approximately (error at most ε) succeed using a polynomial number of samples.

#pac learning#agnostic learning#vc dimension+12

📚TheoryIntermediate

Bias-Variance Tradeoff

The bias–variance tradeoff explains how prediction error splits into bias squared, variance, and irreducible noise for squared loss.

#bias variance tradeoff#mse decomposition#polynomial regression+12

📚TheoryIntermediate

Game Theory

Game theory studies strategic decision-making among multiple players where each player’s payoff depends on everyone’s actions.

#game theory#nash equilibrium#mixed strategies+11

📚TheoryIntermediate

Lagrangian Duality

Lagrangian duality turns a constrained minimization problem into a related maximization problem that provides lower bounds on the original objective.

#lagrangian duality#kkt conditions#slater condition+11

📚TheoryIntermediate

Matrix Calculus

Matrix calculus extends ordinary calculus to functions whose inputs and outputs are vectors and matrices, letting you compute gradients, Jacobians, and Hessians systematically.

#matrix calculus#gradient#jacobian+12

📚TheoryIntermediate

Singular Value Decomposition (SVD)

Singular Value Decomposition (SVD) factors any m×n matrix A into A = UΣV^{T}, where U and V are orthogonal and Σ is diagonal with nonnegative entries.

#singular value decomposition#svd#truncated svd+12

📚TheoryIntermediate

Convex Optimization

Convex optimization studies minimizing convex functions over convex sets, where every local minimum is guaranteed to be a global minimum.

#convex optimization#convex function#convex set+12