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Rust’s strengths in safety, performance, and concurrency make it an excellent choice for machine learning (ML) and numerical computing. In this tutorial, we’ll take a hands-on approach to building a simple feedforward neural network in Rust. Along the way, we’ll cover essential neural network concepts and demonstrate how to harness Rust’s power for ML applications.
Overview
We’ll build a neural network capable of learning the XOR function, which is a classic test for networks’ ability to model non-linear relationships. Our model will be a multi-layer perceptron (MLP) with the following features:
- A Fully Connected Architecture: Each layer connects all neurons to the next.
- Customisable Activation Function: We’ll use the sigmoid function for non-linearity.
- Backpropagation and Gradient Descent: Our network will learn by adjusting weights and biases.
This tutorial will help you understand:
- How to create neural network layers with weights and biases
- The workings of forward and backward passes
- How to measure and reduce error using mean squared error
