Math Foundations
The mathematical tools that underlie every neural network — linear algebra for weight matrices, probability theory for distributions, calculus for optimization, and information theory for measuring uncertainty.
In This Section
Linear Algebra for ML
Vectors, matrices, dot products, eigenvalues, and SVD — the operations neural networks perform at every layer.
Probability & Statistics for ML
Distributions, Bayes' theorem, MLE, and expectation — the statistical foundations of learning from data.
Calculus & Optimization for ML
Gradients, chain rule, convexity, and saddle points — how neural networks find good solutions.
Information Theory Basics
Entropy, cross-entropy, KL divergence, and mutual information — measuring uncertainty and information.