Implementing a Neural Network from Scratch in C++: Understanding the Underlying Principles of Deep Learning

This project demonstrates how to implement a neural network from scratch in pure C++ without relying on frameworks like TensorFlow or PyTorch, helping developers gain an in-depth understanding of the underlying mechanisms of core algorithms such as backpropagation and gradient descent.

• Original Author/Maintainer: AyushBajaj1
• Source Platform: GitHub
• Original Title: neural-network-custom
• Original Link: https://github.com/AyushBajaj1/neural-network-custom
• Publication Date: 2026-06-01

In today's era of highly advanced deep learning frameworks, building a neural network using PyTorch, TensorFlow, or Keras takes only a few lines of code. However, this convenience also brings the "black box" problem—many developers can call APIs to train models but have only a superficial understanding of the underlying principles.

Implementing a neural network from scratch is the best way to understand the core mechanisms of deep learning. By writing algorithms like forward propagation, backpropagation, and gradient descent by hand, developers can build an intuitive understanding of neural networks, which is highly beneficial for subsequent model tuning, architecture design, and problem troubleshooting.

AyushBajaj1's project is exactly such an educational implementation.

neural-network-custom is a neural network project implemented in pure C++ without relying on any external machine learning libraries. The project demonstrates how to build a fully connected neural network from scratch, including network architecture definition, activation function implementation, forward propagation, backpropagation, and training loops.

The project has a concise structure, including source code, Makefile build configuration, and sample datasets. Users can clone, compile, and run it in a few minutes.

The project implements the classic Multi-Layer Perceptron (MLP) architecture, supporting custom number of layers and number of neurons per layer. The network uses fully connected layers, where each neuron is connected to all neurons in the next layer.

The project implements commonly used activation functions such as Sigmoid or ReLU, which are used to introduce non-linear transformations, enabling the network to learn complex non-linear mapping relationships.

The forward propagation algorithm calculates the network output: input data is passed layer by layer from the input layer, undergoing weight matrix multiplication, bias addition, and activation function transformation, finally reaching the output layer. The implementation in the project demonstrates the core role of matrix operations in neural networks.

Backpropagation is the core algorithm for neural network training. It uses the chain rule to calculate gradients layer by layer from the output layer to the input layer, determining the contribution of each weight to the final error. The implementation in the project clearly shows how gradients "propagate" back from the output end to the input end.