Building a Neural Network from Scratch: An Analysis of the micro-grad Project

This article analyzes the educational codebase micro-grad, whose core goal is to help learners intuitively understand the essence of backpropagation and automatic differentiation in neural networks through a streamlined implementation. The project is maintained by sourav-s-b, with source code hosted on GitHub (link: https://github.com/sourav-s-b/micro-grad), and was updated on 2026-05-29. It is not a production-grade framework but a learning tool focused on underlying principles.

Project Origin

• Original author/maintainer: sourav-s-b
• Source platform: GitHub
• Original link: https://github.com/sourav-s-b/micro-grad
• Update time: 2026-05-29T14:12:35Z

Project Overview

micro-grad is a lightweight automatic differentiation implementation (micro + gradient), designed specifically for learning neural network concepts, stripping core mechanisms down to their pure form.

Significance of Implementing from Scratch

Directly using high-level APIs like PyTorch/TensorFlow can easily obscure internal mechanisms. micro-grad provides a bottom-up learning path, helping to build an intuitive understanding of gradient descent, backpropagation, and computation graphs.

Automatic differentiation is the cornerstone of deep learning frameworks. Unlike symbolic/numerical differentiation, it propagates gradients through computation graphs using the chain rule, balancing precision and efficiency.

The core abstraction of micro-grad: each tensor (or scalar) stores its value, operation, and gradient. Forward computation synchronously builds the computation graph, and during backpropagation, gradients flow from the output to the input. This design captures the core idea of PyTorch autograd, aiding in source code reading and model debugging.

The complete training flow includes:

1. Forward propagation: Inputs are transformed through layers to get predicted outputs
2. Loss calculation: Measure the gap between predictions and true values
3. Backpropagation: Calculate the contribution of parameters to the loss
4. Parameter update: Adjust weights using gradient descent

In micro-grad, these steps are explicit Python code, allowing learners to track data flow and gradient flow, observe the weight optimization process, and understand issues like gradient vanishing/exploding.

Educational Value

Bridges the gap between 'using frameworks' and 'understanding principles', making abstract concepts tangible.

Practical Significance

• An ideal platform for prototype verification and algorithm experiments, enabling rapid iteration of new optimization algorithms or structures
• Understanding underlying principles helps developers make informed design decisions in production frameworks.

Recommended learning path:

1. Read through the code to understand the responsibilities of classes and methods
2. Modify the network structure and observe the impact on training results
3. Implement additional features (e.g., different activation functions, regularization)
4. Compare with PyTorch implementations to understand boundary handling and performance optimizations in industrial-grade frameworks

Progressive learning transforms theoretical knowledge into practical skills, laying the foundation for research on complex models.