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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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Rényi Entropy & Divergence

Rényi entropy generalizes Shannon entropy by measuring uncertainty with a tunable emphasis on common versus rare outcomes.

#renyi entropy#renyi divergence#shannon entropy+12
📚TheoryAdvanced

Maximum Entropy Principle

The Maximum Entropy Principle picks the probability distribution with the greatest uncertainty (entropy) that still satisfies the facts you know (constraints).

#maximum entropy principle
Intermediate
Advanced
Filtering by:
#log-sum-exp
Group:
Information Theory
#jaynes
#exponential family
+12
📚TheoryIntermediate

Cross-Entropy

Cross-entropy measures how well a proposed distribution Q predicts outcomes actually generated by a true distribution P.

#cross-entropy#entropy#kl divergence+12