Automatic Differentiation
Showing 36 of 74 projects
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
Autograd automatically differentiates native Torch code, enabling automatic gradient computation for machine learning models.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
A Python library for constructing reactive dataflow graphs and streaming computations as data models.
An image processing library built on JAX, designed to be optimized and parallelized with JAX transformations.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
A high-level Python framework for formulating, optimizing, and executing variational quantum algorithms on simulators and real hardware.
A high-performance C++ automatic differentiation library for large-scale, performance-critical systems.
A high-performance C++ automatic differentiation library for large-scale, performance-critical systems.
A tutorial demonstrating how to extend JAX with custom C++ and CUDA operations for high-performance computing.
An extremely lightweight Gaussian Process library for Python built on JAX with GPU acceleration and automatic differentiation.
An efficient open-source Python package for 3D photonic nanostructure simulation and design using GPU-accelerated FDTD with automatic differentiation.
A JAX library implementing Lie groups for rigid body transformations in computer vision and robotics.
A Python library for GPU-accelerated and differentiable quantum systems simulation built with JAX.
A differentiable cosmology library built with JAX for automatic differentiation of cosmological calculations.
A layer library for JAX-based machine learning projects, optimized for large-scale ML.
A core scientific computing library for Crystal providing n-dimensional tensors, linear algebra, GPU acceleration, and automatic differentiation.
A JAX-based framework for building differentiable numerical simulators with arbitrary discretizations for physical systems.
A symbolic math library and computer algebra system for Rust, providing symbolic differentiation, integration, equation solving, and more.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A Haskell library for building and training feed-forward neural networks with automatic differentiation.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A lightweight Bayesian optimization library built on JAX for efficient optimization of expensive-to-evaluate functions.
A minimal pure Python implementation of reverse-mode automatic differentiation (autograd) for educational purposes.
A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.
A neural network framework with automatic differentiation for building and training models in pure Object Pascal.
A learning-focused, high-performance tensor computation library built from scratch in Rust with automatic differentiation and CPU/CUDA backends.
Kernex extends JAX with kmap and kscan for differentiable stencil computations, enabling efficient array transformations.
A photovoltaic simulator with automatic differentiation for solar cell modeling and optimization, built on JAX.
GPU/TPU accelerated nonlinear least-squares curve fitting using JAX, designed as a drop-in replacement for SciPy's curve_fit.
Composable kernels for scikit-learn implemented in JAX, enabling faster kernel computations and automatic differentiation.
A Python library for accelerated fluid-structure interaction simulations using the immersed boundary lattice Boltzmann method, powered by JAX.
A high-performance JAX-based library for computing optical properties of multilayer thin-film structures using the transfer matrix method.
A 1D3V particle-in-cell simulation code for plasma physics, accelerated using JAX for performance on CPUs, GPUs, and TPUs.
MXNet bindings for the Crystal programming language, enabling deep learning and machine learning development.
A Julia framework for MCMC sampling and optimization with automatic gradient generation for user-defined models.
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