#neural-networks

Emulate simulations easily

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

Relax! Flux is the ML library that doesn't make you tensor

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

The nnsight package enables interpreting and manipulating the internals of deep learned models.

💫 Industrial-strength Natural Language Processing (NLP) in Python

JAX-based neural network library

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Born is a modern ML framework for Go — train and deploy models as single binaries. Pure Go, zero CGO, GPU accelerated.

A new library for all of the newest ai algorithms

Message Passing Neural Networks for Molecule Property Prediction

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/