There are 25 repositories under automatic-differentiation topic.
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
Self-contained Machine Learning and Natural Language Processing library in Go
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control
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
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
Forward Mode Automatic Differentiation for Julia
A simple library for creating complex neural networks
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
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.
Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Tensors and differentiable operations (like TensorFlow) in Rust
A Programming Language for Deep Learning
⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
The Emmy Computer Algebra System.
Reverse Mode Automatic Differentiation for Julia
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Taylor polynomial expansions in one and several independent variables.
Surrogate modeling and optimization for scientific machine learning (SciML)