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

Source-to-Source Debuggable Derivatives in Pure Python

automatic differentiation made easier for C++

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

KErnel OPerationS, on CPUs and GPUs, with autodiff and without memory overflows

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.

pure-Python HistFactory implementation with tensors and autodiff

Drop-in autodiff for NumPy.

A JIT compiler for hybrid quantum programs in PennyLane

Autodifferentiation package in Rust.

[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp

Automatic differentiation of implicit functions

A minimalist deep learning library written from scratch in Python

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

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

Solve ODEs fast, with support for PyMC

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

Automatic differentiation + optimization

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

Universal, autodiff-native software components for Simulation Intelligence. 📦