Deep learning in Rust, with shape checked tensors and neural networks
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Updated
Jul 23, 2024 - Rust
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 neural network, and tensor dynamic automatic differentiation implementation for Rust.
A Deep Learning Framework Written in Rust
Define-by-run arbitrary higher order autodiff for scalars in Rust. Deferred: tensor calculus implementation.
A small scalar autograd engine / Rust crate, inspired from Karpathy's micrograd, with more features, such as more activation functions, optimizers and loss criterions. Capable of MNIST classification.
Automatic differentiation for tensor operations
(WIP) Simple Deep Learning Framework and Auto Differentiation Engine in Rust
A toy neural networks library with zero* dependencies
A tiny autograd engine for learning purposes in Rust
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
A minimal autograd implementation in rust
Automatic differentiation in Rust for educational purposes. Autograd / tinygrad / micrograd / gradients.
RUNE: RUsty Neural Engine
Rust port of Karpathy's micrograd & associated stuff.
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