autodiff

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

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

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.

Autodifferentiation package in Rust.

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

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.

Automatic differentiation of implicit functions