Building Neural Networks from Scratch Demystifies Deep Learning Mechanics

Many modern deep learning frameworks abstract away the core mechanics of neural networks, creating a knowledge gap for developers. This research presents a complete neural network implementation built from the ground up, avoiding reliance on pre-built modules or automatic differentiation. The framework includes essential components like multi-layer architectures, various activation functions, regularization techniques, and advanced optimizers. It aims to clarify concepts such as forward/backward propagation, gradient dynamics, and optimization landscapes. Beyond its educational value, the framework proves its effectiveness and numerical stability on a multi-class classification task, validating its design and generalization capabilities across different configurations. Its modularity also positions it as a strong baseline for further educational and research endeavors.