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Concepts7

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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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All Beginner Intermediate

📚TheoryAdvanced

Transfer Learning Theory

Transfer learning theory studies when and why a model trained on a source distribution will work on a different target distribution.

#transfer learning#domain adaptation#hΔh-divergence+12

⚙️AlgorithmIntermediate

Gibbs Sampling

Gibbs sampling is an MCMC method that generates samples by repeatedly drawing each variable from its conditional distribution given the others.

#gibbs sampling

Filtering by:

#effective sample size

#mcmc

#markov chain

+12

⚙️AlgorithmIntermediate

Metropolis-Hastings Algorithm

Metropolis–Hastings is a clever accept/reject method that lets you sample from complex probability distributions using only an unnormalized density.

#metropolis-hastings#mcmc#acceptance ratio+12

⚙️AlgorithmIntermediate

Markov Chain Monte Carlo (MCMC)

MCMC builds a random walk (a Markov chain) whose long-run visiting frequency matches your target distribution, even when the target is only known up to a constant.

#mcmc#metropolis-hastings#gibbs sampling+12

⚙️AlgorithmIntermediate

Importance Sampling

Importance sampling rewrites an expectation under a hard-to-sample distribution p as an expectation under an easier distribution q, multiplied by a weight w = p/q.

#importance sampling#proposal distribution#self-normalized+12

⚙️AlgorithmIntermediate

Monte Carlo Estimation

Monte Carlo estimation approximates an expected value by averaging function values at random samples drawn from a probability distribution.

#monte carlo#expectation#variance reduction+12

📚TheoryAdvanced

MCMC Theory

MCMC simulates a Markov chain whose long-run behavior matches a target distribution, letting us sample from complex posteriors without knowing the normalization constant.

#mcmc#metropolis-hastings#gibbs sampling+11