automatic differentiation made easier for C++
Deep learning in Rust, with shape checked tensors and neural networks
Tensors and dynamic neural networks in pure Rust.
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.
Fast, easy automatic differentiation in C++
Drop-in autodiff for NumPy.
FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
XLuminA, a highly-efficient, auto-differentiating discovery framework for super-resolution microscopy.
Differentiate python calls from Julia
Fazang is a Fortran library for reverse-mode automatic differentiation, inspired by Stan/Math library.
A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
[wip] Lightweight Automatic Differentiation & DeepLearning Framework implemented in pure Julia.
Forward mode automatic differentiation for Fortran
Yaae: Yet another autodiff engine (written in Numpy).
A differentiable underwater vehicle dynamics.
A minimalist neural networks library built on a tiny autograd engine
JAX Tutorial notebooks : basics, crash & tips, usage of optax/JaxOptim/Numpyro
Algorithmic differentiation with hyper-dual numbers in C++ and Python
A rust implementation of Andrej Karpathy's Micrograd
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