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

Bayesian inference with probabilistic programming.

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

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

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

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

Julia Implementation of Transformer models

Julia library for stylized terminal output

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

Data structures in Julia to enable power systems analysis. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

XGBoost Julia Package

Julia for optimization simulation and modeling of PowerSystems. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

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

Graph Neural Networks in Julia

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

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 package to run Dynamic Power System simulations. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

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

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.

Web server to run just the `@bind` parts of a Pluto.jl notebook

Easy benchmark tracking for packages

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.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A flexible neural net training library inspired by fast.ai

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.