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VC Dimension
ML Math Derivations (20): Regularization and Model Selection
The series finale: from the bias-variance decomposition to L1/L2 geometry, dropout as a sub-network sampler, k-fold CV, AIC/BIC, VC bounds, and the modern double-descent phenomenon that broke classical theory.
ML Math Derivations (1): Introduction and Mathematical Foundations
Why can machines learn from data at all? This first chapter builds the mathematical theory of learning from first principles -- problem formalization, loss surrogates, PAC learning, VC dimension, the bias-variance …