autodiff

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

Source-to-Source Debuggable Derivatives in Pure Python

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

automatic differentiation made easier for C++

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

AutoBound automatically computes upper and lower bounds on functions.

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

An interface to various automatic differentiation backends in Julia.

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

Tensors and dynamic Neural Networks in Mojo

Small deep learning library written from scratch in Python, using NumPy/CuPy.

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