Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

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++

DiffSharp: Differentiable Functional Programming

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

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

End-to-end Generative Optimization for AI Agents

Drop-in autodiff for NumPy.

An interface to various automatic differentiation backends in Julia.

Autodifferentiation package in Rust.

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

A JIT compiler for hybrid quantum programs in PennyLane

Tensors and dynamic Neural Networks in Mojo

Automatic differentiation of implicit functions

Minimal 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