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.

Differentiate python calls from Julia

XLuminA, a highly-efficient, auto-differentiating discovery framework for super-resolution microscopy.

200行写一个自动微分工具

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.

Yaae: Yet another autodiff engine (written in Numpy).

Forward mode automatic differentiation for Fortran

A minimalist neural networks library built on a tiny autograd engine

A differentiable underwater vehicle dynamics.

Algorithmic differentiation with hyper-dual numbers in C++ and Python

JAX Tutorial notebooks : basics, crash & tips, usage of optax/JaxOptim/Numpyro

C++20 numerical and analytical derivative computations

Scala embedded universal probabilistic programming language

A rust implementation of Andrej Karpathy's Micrograd

There's more to JAX.

Experiments with forward gradients on optimization test functions

Assignments for Data Intensive Systems for Machine Learning Coursework

Automatic differentiation: A tool that allows you to calculate multivariable equations, vectors, matrices, and more. All done in C++, no libraries!

A tiny autograd library made for educational purposes.

Reversed mode second order automatic differentiation for python (WIP)

F-1 method

yacc lex for reversed automatic differentiation