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Concepts6

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

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🔷All∑Math⚙️Algo🗂️DS📚Theory

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AllBeginnerIntermediate
⚙️AlgorithmIntermediate

Stratified & Latin Hypercube Sampling

Stratified sampling reduces Monte Carlo variance by dividing the domain into non-overlapping regions (strata) and sampling within each region.

#stratified sampling#latin hypercube sampling#variance reduction+11
⚙️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
Advanced
Filtering by:
#monte carlo
#accept-reject
#proposal distribution
+11
⚙️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
⚙️AlgorithmIntermediate

Bootstrap & Resampling Methods

Bootstrap is a resampling method that estimates uncertainty by repeatedly sampling with replacement from the observed data.

#bootstrap#resampling#confidence intervals+12
⚙️AlgorithmIntermediate

Randomized Algorithms

Randomized algorithms use coin flips (random bits) to guide choices, often making code simpler and fast on average.

#randomized algorithms#las vegas#monte carlo+12