Rust

Multi-Layer Perceptron in Rust

AI Core

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

⏱️ 6h 20min

📦 19 modules

Full Version

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.

Playground Contents

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

Multi-Layer Perceptron in Rust

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