Building a Neural Network from Scratch: Implementation, Evaluation, and Optimization
Machine LearningArtificial Intelligence
Summary
The authors created a simple neural network program built entirely from the ground up, without using any automatic tools or pre-made libraries. Their program includes all the basics needed for neural networks, like different layers, activation functions, and optimizers. They use this framework to help explain how neural networks learn and work internally, especially the math behind training. The program also performs well on a task that involves sorting items into multiple categories, showing it works correctly and reliably. Overall, the authors designed it as an easy-to-understand tool for learning and future improvements.
Authors
Yuanzhe Jia
Abstract
The widespread adoption of high-level deep learning libraries, while accelerating model development, has increasingly abstracted away the internal mechanics of neural networks, creating a gap between practical usage and fundamental understanding. To address this, the paper presents a self-contained neural network framework implemented entirely from scratch -- without relying on automatic differentiation or pre-built deep learning modules. The implementation encompasses all essential components, including multi-layer architectures, diverse activation functions, regularization techniques, and state-of-the-art optimizers. Beyond serving as a pedagogical instrument that demystifies forward/backward propagation, gradient dynamics, and optimization landscapes, the framework demonstrates robust performance when applied to a multi-class classification task, successfully validating its correctness, numerical stability, and generalization across varied configurations. The extensible design and clean modularity further position it as a reliable baseline for educational purposes and future research exploration.