Differentiable matrix factorizations using ImplicitDifferentiation.jl.

Parallel Reverse Mode Automatic Differentiation in C# for Custom Neural Network Development

A modular C++17 framework for automatic differentiation

C++ automatic differentiation library with no dependencies and arbitrary higher order derivatives, stand-alone, header only

Tiny autograd engine written in Go.

Reverse-mode autodiff for Clojure

Yet another automatic differentiation engine to perform efficient and analytically precise partial differentiation of mathematical expressions.

Lightweight Python package for automatic differentiation

Understanding neural network libraries and the automatic gradient computations (autograd) in the backward pass

nanograd is a minimal deep learning framework implementing reverse-mode automatic differentiation and neural network layers from scratch in Python.

A scalar reverse-mode automatic differentiation library with neural network support, built from scratch with PyTorch-like API.

Reverse-mode autograd and small neural-net framework in pure NumPy. Higher-order gradients and a tiny GPT.

A library for reverse mode automatic differentiation

A C++20 template library for symbolic mathematical expressions, derivatives, and equation systems with compile-time evaluation capabilities.