Building a Neural Network from Scratch: A Complete Practice to Deeply Understand Core Deep Learning Principles

This article introduces a neural network project implemented purely in Python without relying on external AI/ML libraries. It demonstrates the underlying implementation of core algorithms like forward propagation, backpropagation, and gradient descent through the Fashion MNIST multi-classification task. The project aims to help readers deeply understand deep learning principles, rather than just staying at the level of using frameworks, making it an excellent learning resource that bridges theory and practice.

Why Build a Neural Network from Scratch

In today's era of mature deep learning frameworks, many practitioners lack a deep understanding of underlying principles. This project chooses a pure Python implementation (only using NumPy for numerical calculations) to allow readers to truly understand the working mechanism of neural networks.

Basic Project Information

• Original Author/Maintainer: bartkw12
• Source Platform: GitHub
• Project Name: Custom-Neural-Network-from-Scratch
• Project URL: https://github.com/bartkw12/Custom-Neural-Network-from-Scratch
• Release Date: May 28, 2026

Core Achievements

Solves the Fashion MNIST multi-classification problem with a test set accuracy of 88.61% (without optimization techniques).

Network Structure

• Input Layer: 784 neurons (flattened from 28×28 pixels)
• Hidden Layers: 1-2 layers (variable)
• Output Layer: 10 neurons (corresponding to 10 categories)
• Activation Functions: ReLU for hidden layers, Softmax for output layer

Forward Propagation

Manually implements linear transformation (z=W·x+b) and activation functions, clearly showing the calculation process.

Backpropagation

Fully implements the chain rule: output layer gradients (derivative of cross-entropy loss), hidden layer gradients (error backpropagation), parameter updates (gradient descent).

Training Process

• Weight Initialization: Xavier/Glorot method
• Training Method: Mini-batch Stochastic Gradient Descent (Mini-batch SGD)
• Learning Rate: Fixed (extension point for scheduling reserved)

Performance Comparison

• Linear Model (e.g., Logistic Regression): ~80% accuracy
• This Project: 88.61% accuracy (without optimization techniques), the gap with modern framework networks of the same scale is acceptable.

Error Patterns

The confusion matrix shows that categories like hoodies vs. shirts, coats vs. shirts are easily confused, reflecting the inherent challenges of the dataset.

Bridge Between Theory and Practice

Fills the gap between deep learning theory (e.g., backpropagation derivation) and code implementation.

Cultivation of Debugging Skills

Issues like dimension mismatches and gradient errors encountered in the from-scratch implementation are the best training for debugging skills.

Deep Understanding of Frameworks

After understanding the underlying layers, one can better recognize the API design and error meanings of PyTorch/TensorFlow.

Possible extension directions:

1. Implement convolutional layers (CNN)
2. Upgrade optimization algorithms (Adam, RMSprop)
3. Regularization techniques (Dropout, batch normalization, L2)
4. GPU acceleration (CuPy or PyTorch low-level APIs)
5. More complex datasets (CIFAR-10/100)

The value of this project lies in the learning process itself, helping readers move from 'knowing what' to 'knowing why'. It is recommended that beginners first use frameworks to build intuition, then study the underlying implementation; experienced practitioners can test their depth of understanding through reproduction. Occasionally returning to the basics and building the foundation by hand can deepen respect and understanding of the structure of deep learning.