Search Results for "automatic differentiation"
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Showing 13 open source projects for "automatic differentiation"
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CasADi
CasADi is a symbolic framework for numeric optimization
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT, etc. It can be used in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and Hessians. ... -
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PETSc.jl
Julia wrappers for the PETSc library
This package provides a low level interface for PETSc and allows combining julia features (such as automatic differentiation) with the PETSc infrastructure and nonlinear solvers. -
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Manifolds.jl
Manifolds.jl provides a library of manifolds
Package Manifolds.jl aims to provide both a unified interface to define and use manifolds as well as a library of manifolds to use for your projects. This package is mostly stable, see #438 for planned upcoming changes. The implemented manifolds are accompanied by their mathematical formulae. The manifolds are implemented using the interface for manifolds given in ManifoldsBase.jl. You can use that interface to implement your own software on manifolds, such that all manifolds based on that... -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. ... -
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RLax
Library of JAX-based building blocks for reinforcement learning agents
RLax (pronounced “relax”) is a JAX-based library developed by Google DeepMind that provides reusable mathematical building blocks for constructing reinforcement learning (RL) agents. Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as... -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
...It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. ... -
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Hasktorch
Tensors and neural networks in Haskell
Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions. Contributions/PR are encouraged. Hasktorch is a library for tensors and neural networks in Haskell. ... -
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spaGO
Self-contained Machine Learning and Natural Language Processing lib
A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to... -
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Random fun
Notebooks and various random fun
Random Fun is a personal collection of experimental scripts and Jupyter notebooks covering machine learning, mathematics, neural networks, and programming ideas. It is not a single application, but a sandbox for compact demonstrations and exploratory work. Included notebooks examine topics such as MicroGrad-style autodiff, mixture density networks, evolution strategies, floating-point behavior, and KNN versus SVMs. Several notebooks explore minimal character-level recurrent neural networks... -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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gradslam
gradslam is an open source differentiable dense SLAM library
...However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable. In this work, we present gradSLAM, a differentiable computational graph take on SLAM. Leveraging the automatic differentiation capabilities of computational graphs, gradSLAM enables the design of SLAM systems that allow for gradient-based learning across each of their components, or the system as a whole. -
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AdaAutoDiff
C++ Templates and Ada Package for Automatic Differentiation
Operators are overloaded so that a normal looking function definition provides access to not only evaluations of itself, but to evaluations of any of its analytic derivatives. Automatic differentiation means the user does not need to define the analytic expressions for all the various partial derivatives. It also means that those complex expressions are essentially calculated at compile time, and merely evaluated at runtime. First order derivatives only, forward accumulation. Choose "files" and either the "initial_submission" directory for the Ada version, or the other directory "cpp_AD" for the C++ version. -
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
...It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The codebase is organized as modular math and finance primitives so you can combine building blocks or target end-to-end examples. It includes Bazel builds, tests, and example notebooks to accelerate learning and adoption in real workflows. With hardware acceleration and differentiable models, it enables modern techniques like gradient-based calibration and end-to-end learning of market dynamics.
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