Rust MiniGrad with Python Bindings

Introduction

Rust MiniGrad is an experimental automatic differentiation library built in Rust with Python bindings. This is just a learning project.

Status: 🚧 Under Construction 🚧

Examples

You can find detailed examples in the examples folder. Here's a quick look:

Matrix Operations

In Rust

```rust use minigrad::matrix::{Matrix, matrix_sum, matrix_multiply};

let a = Matrix::new(2, 2, vec![vec![1.0, 2.0], vec![3.0, 4.0]]); let b = Matrix::new(2, 2, vec![vec![2.0, 3.0], vec![4.0, 5.0]]);

let sum = matrix_sum(&a, &b); let product = matrix_multiply(&a, &b);

println!("{}", sum); println!("{}", product); ```

In Python

``` import homeydl

matrix1 = homeydl.Matrix(2, 2, [[1.0, 2.0], [3.0, 4.0]]) matrix2 = homeydl.Matrix(2, 2, [[2.0, 0.0], [1.0, 3.0]])

sum = matrix1 + matrix2 product = matrix1 * matrix2

print(sum) print(product) ```

Automatic Differentiation

[ ] Yet to be implemented.

Installation

```bash git clone https://github.com/your_username/rust_minigrad.git cd rust_minigrad cargo build --release ```

For Python bindings you can install matrurin and run:

```bash matrurin develop ```

TODOs

• Implement core matrix operations.
• Add support for more matrix operations.
• Add support for automatic differentiation.
• Integrate GPU acceleration.
• Add python bindings.
• Improve Python bindings for easier integration.
• Add more examples and benchmarks.
• Write some sort of docs and references?.