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Gradient Descent
Machine Learning Mathematical Derivations (6): Logistic Regression and Classification
Complete derivation of logistic regression from sigmoid to softmax, cross-entropy loss, gradient computation, regularization, and multi-class extension with Python verification.
Mathematical Derivation of Machine Learning (5): Linear Regression
A complete derivation of linear regression from three perspectives -- algebra (the normal equation), geometry (orthogonal projection), and probability (maximum likelihood) -- followed by Ridge, Lasso, gradient methods, …
ML Math Derivations (4): Convex Optimization Theory
Nearly every ML algorithm is an optimization problem. This article derives convex sets, convex functions, gradient descent, Newton's method, KKT conditions, and ADMM -- the optimization toolkit for machine learning.