Building a Neural Network from Scratch: Implementation, Evaluation, and Optimization

• URL: http://arxiv.org/abs/2607.16682v1
• Date: Sat, 18 Jul 2026 07:31:59 GMT
• Title: Building a Neural Network from Scratch: Implementation, Evaluation, and Optimization
• Abstract summary: The paper presents a self-contained neural network framework implemented entirely from scratch.<n>The implementation encompasses all essential components, including multi-layer architectures, diverse activation functions, and state-of-the-art modularitys.
• Score: 0.913755431537592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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