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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

Level

All Beginner Intermediate

📚TheoryIntermediate

Embedding Spaces & Distributed Representations

Embedding spaces map discrete things like words or products to dense vectors so that similar items are close together.

#embeddings#dense vectors#cosine similarity+12

📚TheoryAdvanced

Random Matrix Theory in High-Dimensional Statistics

Random Matrix Theory (RMT) explains how eigenvalues of large random matrices behave when the dimension p is comparable to the sample size n.

#random matrix theory

Filtering by:

#pca

#marchenko-pastur

#wigner semicircle

+12

∑MathAdvanced

Marchenko-Pastur Distribution

The Marchenko–Pastur (MP) distribution describes the limiting eigenvalue distribution of sample covariance matrices S = (1/n) XX^{\top} when both the dimension p and the sample size n grow with p/n \to \gamma.

#marchenko-pastur#random matrix theory#sample covariance+10

∑MathAdvanced

Manifolds & Manifold Hypothesis

A manifold is a space that locally looks like Euclidean space, stitched together by coordinate charts and smooth transition maps.

#manifold#topological manifold#smooth manifold+12

∑MathIntermediate

Low-Rank Approximation

Low-rank approximation replaces a big matrix with one that has far fewer degrees of freedom while preserving most of its action.

#low-rank approximation#eckart-young theorem#svd+12

∑MathIntermediate

Eigendecomposition

Eigendecomposition expresses a matrix as a change of basis times a diagonal scaling, revealing its natural stretching directions.

#eigendecomposition#eigenvalue#eigenvector+11

📚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

Eigenvalue Decomposition

Eigenvalue decomposition rewrites a square matrix as a change of basis that reveals how it stretches and rotates space.

#eigenvalue decomposition#spectral theorem#power iteration+12

📚TheoryIntermediate

Linear Algebra Theory

Linear algebra studies vectors, linear combinations, and transformations that preserve addition and scalar multiplication.

#linear algebra#vector space#basis+12