Automatic Differentiation
Showing 28 of 64 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.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
An image processing library built on JAX, designed to be optimized and parallelized with JAX transformations.
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.
A JAX library implementing Lie groups for rigid body transformations in computer vision and robotics.
An efficient open-source Python package for 3D photonic nanostructure simulation and design using GPU-accelerated FDTD with automatic differentiation.
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 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 production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
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.
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