Rust

Single-Layer Perceptron in Rust

A straightforward guide to building a perceptron system that learns to perform basic logical operations such as AND and OR.

⏱️ 3h 0min

📦 9 modules

🎯 Intermediate

What You'll Build

We'll build a tiny but complete learning machine: a single-layer perceptron packaged as a reusable Rust library crate. Starting from an empty project, we'll define the data structure, initialize random weights, implement the forward pass and training loop, then verify everything with integration tests for AND, OR, and XOR logic gates.

Along the way we'll see how the perceptron learning rule adjusts weights, why convergence is guaranteed for linearly separable problems, and what happens when a problem (XOR) isn't linearly separable.

Learning Objectives

Set up a Rust library crate from scratch
Define a generic perceptron struct with const generics
Initialize weights and bias randomly with rand
Implement the forward pass with weighted sum and step activation
Implement the perceptron learning rule with early stopping
Write integration tests for AND, OR, and XOR logic gates
Understand the linear separability limitation of single-layer perceptrons

Prerequisites

Basic Rust syntax (structs, impl blocks, generics)
Rust toolchain and cargo installed
Comfort with arrays, slices, and iterators
Basic understanding of binary classification

Assembly Steps

Project Baseline

Perceptron Data Model

Weight Initialization

API Contract

AND Gate Verification

Forward Computation

Learning Rule

OR Gate Generalization

XOR Limitation

Technologies

Rust Perceptron Machine Learning Binary Classification Activation Function Learning Rate Weights and Bias Linear Separability Const Generics rand