๐ŸŽ“How I Study AIHISA
๐Ÿ“–Read
๐Ÿ“„Papers๐Ÿ“ฐBlogs๐ŸŽฌCourses
๐Ÿ’กLearn
๐Ÿ›ค๏ธPaths๐Ÿ“šTopics๐Ÿ’กConcepts๐ŸŽดShorts
๐ŸŽฏPractice
โฑ๏ธCoach๐ŸงฉProblems๐Ÿง Thinking๐ŸŽฏPrompts๐Ÿง Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts4

Groups

๐Ÿ“Linear Algebra15๐Ÿ“ˆCalculus & Differentiation10๐ŸŽฏOptimization14๐ŸŽฒProbability Theory12๐Ÿ“ŠStatistics for ML9๐Ÿ“กInformation Theory10๐Ÿ”บConvex Optimization7๐Ÿ”ขNumerical Methods6๐Ÿ•ธGraph Theory for Deep Learning6๐Ÿ”ตTopology for ML5๐ŸŒDifferential Geometry6โˆžMeasure Theory & Functional Analysis6๐ŸŽฐRandom Matrix Theory5๐ŸŒŠFourier Analysis & Signal Processing9๐ŸŽฐSampling & Monte Carlo Methods10๐Ÿง Deep Learning Theory12๐Ÿ›ก๏ธRegularization Theory11๐Ÿ‘๏ธAttention & Transformer Theory10๐ŸŽจGenerative Model Theory11๐Ÿ”ฎRepresentation Learning10๐ŸŽฎReinforcement Learning Mathematics9๐Ÿ”„Variational Methods8๐Ÿ“‰Loss Functions & Objectives10โฑ๏ธSequence & Temporal Models8๐Ÿ’ŽGeometric Deep Learning8

Category

๐Ÿ”ทAllโˆ‘Mathโš™๏ธAlgo๐Ÿ—‚๏ธDS๐Ÿ“šTheory

Level

AllBeginner
โš™๏ธAlgorithmIntermediate

Numerical Differentiation & Finite Differences

Numerical differentiation uses finite differences to estimate derivatives when an analytical derivative is hard or impossible to obtain.

#numerical differentiation#finite differences#forward difference+12
โš™๏ธAlgorithmIntermediate

Numerical Integration & Monte Carlo

Numerical integration approximates the area under a curve when an exact antiderivative is unknown, using deterministic quadrature rules or random sampling (Monte Carlo).

Intermediate
Advanced
Group:
Numerical Methods
#numerical integration
#quadrature
#trapezoidal rule
+11
โš™๏ธAlgorithmIntermediate

Matrix Factorizations (Numerical)

Matrix factorizations rewrite a matrix into simpler building blocks (triangular or orthogonal) that make solving and analyzing linear systems much easier.

#lu decomposition#qr factorization#householder reflections+12
โš™๏ธAlgorithmIntermediate

Iterative Methods for Linear Systems

The Conjugate Gradient (CG) method solves large, sparse, symmetric positive definite (SPD) linear systems Ax = b using only matrixโ€“vector products and dot products.

#conjugate gradient#iterative solver#krylov subspace+12