Automatic Differentiation
Showing 36 of 64 projects
A Python package for tensor computation with GPU acceleration and dynamic neural networks built on a tape-based autograd system.
A Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and machine learning.
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
An array framework for machine learning on Apple silicon with unified memory and dynamic graph construction.
A unified deep learning toolkit for describing neural networks as computational graphs, supporting feed-forward DNNs, CNNs, and RNNs/LSTMs.
A low-level tensor library for machine learning with integer quantization, automatic differentiation, and zero runtime allocations.
WebGL-accelerated machine learning library for JavaScript with linear algebra and automatic differentiation.
Automatically differentiate native Python and NumPy code for gradient-based optimization and machine learning.
An archived experiment integrating TensorFlow's machine learning capabilities directly into the Swift programming language with first-class differentiable programming.
A flexible Python deep learning framework using define-by-run dynamic computational graphs for neural network research.
A library for building and evaluating mathematical expressions and neural networks in Go, with automatic differentiation and GPU support.
Rust bindings for the C++ API of PyTorch, providing thin wrappers around libtorch.
A cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry.
A cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry.
An open-source Python library for quantum computing, quantum machine learning, and quantum chemistry.
A JAX-based neural network library that provides a simple, object-oriented programming model for building and training models.
A Python library for automatic differentiation that generates readable Python source code as its derivative output.
A gradient processing and optimization library for JAX, designed for research with composable building blocks.
A symbolic framework for numeric optimization with automatic differentiation and code generation capabilities.
A curated list of awesome libraries, projects, tutorials, and resources for the JAX machine learning ecosystem.
A JAX-based library providing numerical differential equation solvers for ODEs, SDEs, and CDEs with autodifferentiation and GPU support.
A C++17 library for automatic differentiation with forward and reverse mode support, enabling efficient derivative computation.
A deep learning library in Rust featuring shape-checked tensors and neural networks with compile-time safety.
A deep learning library for Rust featuring shape-checked tensors and neural networks with compile-time safety.
A self-contained machine learning and natural language processing library written in pure Go with a dynamic computational graph.
An efficient C++ library for robotics, optimal control, and model predictive control with a focus on online performance.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
A dedicated OCaml system for scientific and engineering computing, providing n-dimensional arrays, linear algebra, algorithmic differentiation, and neural networks.
A deep learning JavaScript library built from scratch with PyTorch-like syntax and GPU acceleration via GPU.js.
An object-oriented machine learning framework built on JAX, designed for simplicity and readability in research.
A lightweight C library for building and training small to medium artificial neural networks with minimal dependencies.
A JAX-powered library for solving large-scale optimal transport problems, including matching, barycenters, and neural approximations.
A Rust numeric library for linear algebra, numerical analysis, statistics, and machine learning with high performance and syntax inspired by R, MATLAB, and Python.
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
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