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

Bayesian inference with probabilistic programming.

A Julia machine learning framework

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

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

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

A Julia wrapper for TensorFlow

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

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

Julia Implementation of Transformer models

An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

Play atmospheric modelling like it's LEGO.

Probabilistic programming via source rewriting

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

Molecular simulation in Julia

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Geometric Deep Learning for Flux

Computer vision models for Flux

Types and utility functions for summarizing Markov chain Monte Carlo simulations

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

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Utility package for accessing common Machine Learning datasets in Julia

Graph Neural Networks in Julia

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

Causal inference, graphical models and structure learning in Julia

A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.

Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.

Implementations of the models from the Statistical Rethinking book with Turing.jl

Causal, Higher-Order, Probabilistic Programming

Core functionality for the MLJ machine learning framework

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Gaussian Process package based on data augmentation, sparsity and natural gradients

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Simple multithreading in julia

A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS)

Complex neural network examples for Flux.jl

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.