Information Theory
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Mon Sep 01 2025
7261 words · 38 minutes
Deep Learning Notes-1: Polynomial Fitting, Probability Theory, and Information Theory Fundamentals
Deep Learning Notes-1, covering overfitting and underfitting, regularization, probability theory fundamentals (Bayes' theorem, Gaussian distribution, maximum likelihood estimation), and information theory (entropy and KL divergence). Corresponds to Chapters 1-2 of "Deep Learning: Foundations and Concepts".