Autodiff GitHub Repositories

Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

automatic differentiation made easier for C++

Deep learning in Rust, with shape checked tensors and neural networks

End-to-end Generative Optimization for AI Agents

Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.

DiffSharp: Differentiable Functional Programming

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Assignment 1: automatic differentiation

Fast, easy automatic differentiation in C++

AutoBound automatically computes upper and lower bounds on functions.

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

Nabla: High-Performance Scientific Computing

An interface to various automatic differentiation backends in Julia.

A JIT compiler for hybrid quantum programs in PennyLane

Drop-in autodiff for NumPy.

Scientific computing that fits on a microcontroller. Estimation, control, kinematics, Lie groups, calculus, autodiff and linear algebra in stable no_std Rust with no heap, no panics and no unsafe. Run the same code on your laptop and your Cortex-M0.

Autodifferentiation package in Rust.

Automatic differentiation of implicit functions

High-Performance LISP-like language for Scientific Computing and AI written in C++

A minimalist deep learning library written from scratch in Python

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.

FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.

Solve ODEs fast, with support for PyMC

An experimental deep learning framework for Nim based on a differentiable array programming language

Automatic differentiation + optimization

A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.

Geometry processing utilities compatible with jax for autodifferentiation.

Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.

Reverse mode autodiff library, interior-point method, and NLP solver DSL for C++23 and Python