Building a Digit Recognition Neural Network from Scratch: A Handwritten Implementation Learning Project

This project is developed by DistancedTurtle, with source code available on GitHub (link: https://github.com/DistancedTurtle/Number-Identification-NN), released on June 9, 2026. The core goal is to implement MNIST digit recognition using pure Python + NumPy without relying on deep learning frameworks like TensorFlow/PyTorch, helping developers deeply understand the underlying principles of neural networks (such as backpropagation, gradient descent, etc.). This thread will introduce the project background, technical implementation, core principles, challenges, learning value, and other content in separate floors.

Today, with the popularity of deep learning frameworks, many developers rely on high-level APIs to build models but lack a deep understanding of core concepts like backpropagation and weight initialization. The original intention of this project is to allow developers to "return to the basics" by implementing a neural network from scratch, re-mastering those easily overlooked fundamental knowledge, and laying a solid foundation for in-depth learning of deep learning.

Core Objectives: Build a neural network that can recognize MNIST handwritten digits 0-9, with the feature of not relying on any deep learning frameworks and implementing all components (forward propagation, backpropagation, etc.) purely in Python. Technology Stack: Python language, NumPy (matrix operations), MNIST dataset, Multi-Layer Perceptron (MLP) architecture.

Network Architecture

Input layer with 784 neurons (corresponding to 28×28 pixels), hidden layer as a fully connected layer (with non-linear activation), output layer with 10 neurons (corresponding to 0-9 classification).

Forward Propagation

Formula: z = W·x + b → a = activation(z) (activation functions like ReLU, Sigmoid).

Loss and Optimization

Use cross-entropy loss function, update parameters via backpropagation (calculate gradients using chain rule) and gradient descent (W_new = W_old - α·∂L/∂W).

1. Numerical Stability: Subtract the maximum value during Softmax calculation to avoid overflow;
2. Weight Initialization: Use Xavier/He initialization to prevent gradient vanishing/exploding;
3. Learning Rate Issue: Need to balance the step size (too large leads to oscillation, too small leads to slow convergence);
4. Efficiency Optimization: Replace loops with NumPy vectorized operations to improve speed.

• Framework Understanding: Understand the underlying logic of automatic differentiation and tensor operations;
• Interview Preparation: Master backpropagation derivation and optimization algorithm principles to handle technical interviews;
• Research Foundation: Provide solid theoretical support for modifying loss functions and designing new architectures.

• Architecture Upgrade: Add convolutional layers (CNN), Dropout regularization, batch normalization;
• Optimizer Improvement: Implement adaptive optimizers like Adam/RMSprop;
• Function Enhancement: Support CIFAR-10 dataset, add model save/load and training visualization tools.

Although this project is simple, it carries important learning value. In today's era of deep learning "black-boxization", the practice of implementing every component by hand is worth trying for all AI practitioners. Whether you are a beginner or a senior developer, building a neural network from scratch is a valuable exercise to review old knowledge and solidify the foundation.