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 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.

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

Automatic differentiation for tensor operations

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

A toy neural networks library with zero* dependencies

(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.