Implementing a 2-Layer Neural Network for MNIST with Numerical Differentiation

This article details the practical implementation of a two-layer neural network using numerical differentiation for the MNIST dataset, a fundamental learning exercise in deep learning. The reliance on a specific textbook suggests a pedagogical approach, targeting those learning the theoretical foundations. The use of Gemini indicates AI-assisted content creation, adding a potentially interesting element to the learning experience.