RUST Essentials Intermediate

Single-Layer Perceptron in Rust

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

Premium · 1h 52m · 5 ch

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What you'll build

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

Along the way, we will see how the perceptron learning rule adjusts weights, why convergence is guaranteed for linearly separable tasks, and what happens when a task (XOR) is not linearly separable.

Learning objectives

✓Set up a Rust library crate from scratch

✓Define a generic perceptron struct with const generics

✓Randomly initialize weights and bias using rand

✓Implement a forward pass with a weighted sum and step activation

✓Implement the perceptron learning rule with early stopping

✓Write integration tests for the AND, OR, and XOR logic gates

✓Understand the limitation of linear separability of single-layer perceptrons

Prerequisites

▸Basic Rust syntax (structs, impl blocks, generics)

▸Rust toolchain and cargo installed

▸Confident work with arrays, slices, and iterators

▸Basic understanding of binary classification

Course curriculum

1 Adjustable Perceptron Weights

—Initializing the Perceptron Library

—Adding the Perceptron Skeleton

—Random Initialization of Initial Weights

2 Perceptron Training Logic

—Adding the Training Loop

—Perceptron Computation

—Training the Perceptron

—Configuring the Learning Rate

—Early stopping of training

3 Prediction Method

—Adding the Prediction Method

4 Training on Logic Gates

—Training the AND Perceptron Example

—Training the OR Perceptron

5 Perceptron Limitations

—Training and Testing XOR

Technologies

Rust Perceptron Machine learning Binary classification Activation function Learning rate Weights and bias Linear separability Const generics rand

FAQ

Is this a video course?

No. You work through the project step by step in your browser, and the reference code for each step saves to your disk — yours to run and keep locally.

Do I need to install anything?

No. The playground runs entirely in your browser — no toolchain or local setup required.

Can I try it before subscribing?

Yes. Preview steps are available without a subscription so you can see the format and quality first.

What do I get with full access?

Every step unlocked, the Workbench app, a snapshot for each step, jump-to-any-step navigation, and all new playground releases.

Does it work with an AI agent like Claude or Codex?

Yes. You can work through a playground solo or alongside an AI agent.

Is there reference code?

Yes. Every step has a complete reference solution you can compare against.