ML/DL Mathematical Foundations

Purpose

Provide rigorous, implementation-focused reference for all mathematical concepts underpinning machine learning and deep learning. Each reference file bridges theory ↔ practice with derivations, NumPy/SciPy code, and direct mapping to ML algorithms.

Agent Protocol

Trigger

User request includes: prove, derive, gradient, backprop, SVD, eigenvalue, eigendecomposition, chain rule, loss function derivative, optimization convergence, KL divergence, entropy, information theory, kernel trick, PCA math, Bayesian inference, EM algorithm, Taylor expansion, attention math, normalization math, initialization math.

Input Context

• Specific math concept or derivation needed
• Algorithm context (e.g., "derivation of Adam", "XGBoost objective", "transformer attention math")
• Current understanding level (conceptual, formula-level, implementation-level)