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++

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

DiffSharp: Differentiable Functional Programming

End-to-end Generative Optimization for AI Agents

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.

Differentiable Programming in Mojo

Drop-in autodiff for NumPy.

Autodifferentiation package in Rust.

A JIT compiler for hybrid quantum programs in PennyLane

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

Automatic differentiation of implicit functions

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