#automatic differentiation

Chainer, a Python-centric deep learning framework, utilizes a define-by-run approach for dynamic computational graphs and automatic differentiation. It supplies high-level APIs for neural network construction and leverages CuPy for superior CUDA-based training and inference. Despite transitioning to a maintenance phase, Chainer remains a robust solution for various deep learning applications, with Docker images ensuring easy deployment and NVIDIA Docker support.

Discover this powerful Python library for quantum computing and machine learning. Utilize just-in-time compilation, automatic differentiation, and a wide range of quantum backends with plugins for IBM Q, Google Cirq, and more. Access extensive tutorials and community resources for rapid prototyping and contributions.

Enzyme is a high-performance tool for automatic differentiation in LLVM and MLIR, designed to enhance gradient computation efficiency. It can be easily integrated via direct build or package managers like Homebrew and Spack. Supporting both Julia and Rust bindings, it is suited for researchers and developers in high-performance computing and machine learning. Discover installation guides and connect with the community for in-depth information and collaborative opportunities.

This comprehensive guide features a wide range of JAX libraries, projects, and resources suited for researchers in high-performance machine learning using GPUs and TPUs. It includes neural network libraries such as Flax, Haiku, and Objax, along with specialized tools like Jraph for graph neural networks and NumPyro for probabilistic programming. The guide also highlights pioneering libraries like FedJAX for federated learning and Levanter for scalable model deployment. Discover models, projects, tutorials, videos, and community resources that make this an essential resource for maximizing the use of JAX.

Endia is a scientific computing library that features automatic differentiation and JIT compilation, suited for advanced complex number applications. It offers both imperative and functional interfaces, akin to PyTorch and JAX. While still in development and not production-ready, Endia aims to advance AI and scientific computing with high-performance, relying solely on Mojo and MAX to simplify integration.