A mathematical primer to classical deep learning

Abstract

This manuscript synthesizes the statistical foundations of classical deep learning by integrating insights from eight seminal works. It covers matrix calculus, neuron layers, weight and bias indexing, cost functions, differentiation of neuron operations, activation functions, bias functions, gradient descent, and backpropagation algorithms. The synthesis aims to provide a comprehensive understanding of the mathematical and statistical principles underpinning deep learning models, facilitating their application and further development in various domains.