automatic-differentiation

Tensor library for machine learning

Gorgonia is a library that helps facilitate machine learning in Go.

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Source-to-Source Debuggable Derivatives in Pure Python

A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives

Self-contained Machine Learning and Natural Language Processing library in Go

automatic differentiation made easier for C++

The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

21st century AD

High-performance automatic differentiation of LLVM and MLIR.

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

Owl - OCaml Scientific Computing @ https://ocaml.xyz

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

A JavaScript library like PyTorch, with GPU acceleration.

Forward Mode Automatic Differentiation for Julia

OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions

Aircraft design optimization made fast through computational graph transformations (e.g., automatic differentiation). Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.

『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)

The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.