How to Use burn for Deep Learning in Rust

Use the burn crate by adding it to your Cargo.toml and initializing a Module with a runtime to define and train models. Add burn = "0.13" to your dependencies, then create a module struct, implement the Module trait, and run it with burn::module::Module::forward.

use burn::module::Module;
use burn::tensor::Tensor;

#[derive(Module, Debug)]
struct MyModel {
    weight: Tensor, // Define parameters here
}

fn main() {
    let model = MyModel::init(); // Initialize with default values
    let input = Tensor::from_floats(&[1.0, 2.0, 3.0]);
    let output = model.forward(&input); // Run inference
    println!("Output: {:?}", output);
}

Burn is a library that lets you build and run machine learning models in Rust. It handles the math and memory management so you can focus on designing your model's structure. Think of it as a toolkit that provides the building blocks for neural networks without needing to write complex math code yourself.