Neural Network from Scratch in Rust

This project implements a simple neural network from scratch using Rust. The goal is for the neural network to learn the XOR function, which means the network should output values close to [0.0] or [1.0] depending on the input pairs. After successful training, the network should approximate these results accurately.

Modules

• activations.rs: Contains the activation functions.
• backprop.rs: Contains the backpropagation logic.
• lib.rs: Defines the neural network structure and integrates the activation functions and backpropagation logic.
• main.rs: Contains the main function to train and test the neural network.

Getting Started

Prerequisites

• Rust (Installation instructions can be found here)

Installing

1. Clone the repository

   git clone https://github.com/eldan1z/rust_nn.git
   cd rust_nn

2. Add dependencies in Cargo.toml

   [dependencies]
   rand = "0.8"

3. Create the necessary source files with the provided content.

Running the Project

1. Build and run the project using Cargo

   cargo run

Example Output

[0.0, 0.0] -> [0.001]
[0.0, 1.0] -> [0.999]
[1.0, 0.0] -> [0.999]
[1.0, 1.0] -> [0.001]