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

Bayesian inference with probabilistic programming.

A Julia machine learning framework

Koç University deep learning framework.

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

CUDA programming in Julia.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

A Julia wrapper for TensorFlow

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

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

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Fast and simple fluid simulator in Julia

Repository of best practices for deep learning in Julia, inspired by fastai

Library for the numerical simulation of closed as well as open quantum systems.

Julia Implementation of Transformer models

A Julia library for efficient tensor computations and tensor network calculations

Climate Machine: an Earth System Model that automatically learns from data

Julia package for tensor contractions and related operations

Play atmospheric modelling like it's LEGO.

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

Molecular simulation in Julia

Geometric Deep Learning for Flux

Metal programming in Julia

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Computer vision models for Flux

XGBoost Julia Package

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

A Curious Cumulation of CUDA Cuisine

AMD GPU (ROCm) programming in Julia

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Utility package for accessing common Machine Learning datasets in Julia

Grid-based simulations in Julia

Graph Neural Networks in Julia

A Julia package for large-scale tensor computations, with a hint of category theory

Sensible extensions for exposing torch in Julia.

Reservoir computing utilities for scientific machine learning (SciML)