Rust

Multi-Layer Perceptron in Rust

Learn how multilayer perceptrons solve non-linear problems using hidden layers and backpropagation through an XOR example.

⏱️ 6h 20min

📦 19 modules

🎯 Intermediate

What You'll Build

We'll build a complete multi-layer perceptron (MLP) neural network from the ground up using Rust. Starting with the mathematical foundations of activation functions, we'll progressively construct layers with const generics, wire them into a network, and implement the full backpropagation training algorithm.

Along the way we'll see why a single perceptron can't learn XOR, how hidden layers create nonlinear decision boundaries, and how gradient descent adjusts weights to minimize loss. By the end, our MLP can learn nonlinear functions — something a single-layer perceptron cannot do.

Learning Objectives

Understand the role of activation functions in neural networks
Implement a generic neural network layer with const generics
Build a multi-layer perceptron with hidden and output layers
Implement backpropagation with gradient descent training
Train a network to learn the XOR function

Prerequisites

Basic Rust syntax (structs, traits, generics)
Familiarity with iterators and closures in Rust
Basic understanding of linear algebra (vectors, dot products)

Assembly Steps

Project Baseline

Activation Interface

Sigmoid Behavior

Activated Layer Model

Parameter Initialization

Forward API Contract

Forward Computation

Single Neuron Baseline

MLP Skeleton

Constructor Design

Core MLP Methods

XOR Learning Target

Training Pipeline Plan

Network Forward Pass

Loss Evaluation

Backpropagation Flow

Gradient Update Step

ReLU Option

Tanh Option

Technologies

Rust Neural Network Multi-Layer Perceptron Backpropagation Activation Function Gradient Descent Sigmoid ReLU Tanh Const Generics