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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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📚TheoryAdvanced

Energy-Based Models (EBM)

Energy-Based Models (EBMs) define probabilities through an energy landscape: low energy means high probability, with p(x) = exp(-E(x)) / Z.

#energy-based models#partition function#langevin dynamics+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
Advanced
Filtering by:
#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

Rejection Sampling

Rejection sampling draws from a hard target distribution by using an easier proposal and accepting with probability p(x)/(M q(x)).

#rejection sampling#accept-reject#proposal distribution+11
📚TheoryIntermediate

Bayesian Inference

Bayesian inference updates prior beliefs with observed data to produce a posterior distribution P(\theta\mid D).

#bayesian inference#posterior#prior+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
📚TheoryIntermediate

Markov Chain Theory

A Markov chain is a random process where the next state depends only on the current state, not the full history.

#markov chain#transition matrix#stationary distribution+12