A straightforward guide to building a perceptron system that learns to perform basic logical operations such as AND and OR.
You'll build a tiny but complete learning machine: a single perceptron packaged as a reusable Rust crate with training, prediction, and example binaries. The model learns the AND and OR logic gates and reveals its limits on XOR, giving you a clear view of how simple decision-makers behave.
You'll use the rand crate for realistic initialization and run examples like cargo run --example and to watch the perceptron improve in real time.
Build a configurable perceptron in Rust
Initialize weights and bias with rand
Compute predictions with a step activation
Train with epochs and learning rate
Evaluate on AND, OR, XOR tasks
Implement early stopping using tolerance
Basic Rust programming knowledge
Rust and cargo installed
Basic command-line experience
Understanding of binary classification
Comfort with arrays and slices
Configurable Perceptron Weights
Set up a perceptron struct with weights, bias, and configuration parameters. Use rand for initialization.
Perceptron Training Logic
Implement the training loop with epochs, learning rate, and weight updates based on prediction errors.
Prediction Method
Build the prediction function using dot product and step activation to classify binary inputs.
Training Logic Gates
Train the perceptron on AND gate data and verify convergence with example binaries.
Training Logical OR
Apply the same training approach to OR gate and compare learning behavior with AND.
Perceptron Limits with XOR
Attempt to train on XOR gate to demonstrate linear separability limits and understand when perceptrons fail.
