Deep learning in Rust, with shape checked tensors and neural networks

Tensors and differentiable operations (like TensorFlow) in Rust

A minimal OpenCL, CUDA, Vulkan and host CPU array manipulation engine / framework.

A Deep Learning framework with very few dependencies, Written in Rust

A learning-focused, high-performance tensor computation library built from scratch in Rust, featuring automatic differentiation and CPU/CUDA backends.

A neural network, and tensor dynamic automatic differentiation implementation for Rust.

Define-by-run arbitrary higher order autodiff for scalars in Rust. Deferred: tensor calculus implementation.

Automatic differentiation for tensor operations

Small scalar autograd engine, inspired from Karpathy's micrograd, with some additional features, such as more activation functions, optimizers and loss criterions.

A Machine Learning framework written in Rust (WIP)

A toy neural networks library with zero* dependencies

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

(WIP) Simple Deep Learning Framework and Auto Differentiation Engine in Rust

Array operations with automatic differentiation on CPU and GPU

Automatic differentiation in Rust for educational purposes. Autograd / tinygrad / micrograd / gradients.

A tiny autograd engine for learning purposes in Rust

A minimal autograd implementation in rust

🦀 RUNE: RUsty Neural Engine

RUNE: RUsty Neural Engine

Rust port of Karpathy's micrograd & associated stuff.