Why Deep Learning Works: The Probability Theory Behind Neural Networks

While most developers treat deep learning as a collection of architectural tricks, the field actually rests on probability theory and information-theoretic foundations. This guide explains how entropy quantifies data complexity, how KL divergence measures actual training progress, and why neural networks fundamentally work as probability distribution matchers—insights that transform how you understand and debug model behavior.