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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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∑MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy#binary cross-entropy#softmax+11
∑MathIntermediate

Maximum Likelihood Estimation (MLE)

Maximum Likelihood Estimation (MLE) chooses parameters that make the observed data most probable under a chosen model.

#maximum likelihood
Intermediate
Advanced
Filtering by:
#maximum likelihood
#log-likelihood
#bernoulli mle
+12
∑MathIntermediate

Exponential Family Distributions

Exponential family distributions express many common probability models in a single template p(x|η) = h(x) exp(η^T T(x) − A(η)).

#exponential family#natural parameter#sufficient statistics+12