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.

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

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

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of 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.

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

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.

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

XGBoost Julia Package

Data structures in Julia to enable power systems analysis. 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

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

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

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

Methods to compute sets of states reachable by dynamical systems

A Bayesian Analysis Toolkit in Julia

Causal, Higher-Order, Probabilistic Programming

Graph Neural Networks in Julia

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

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

Differentiable RayTracing in Julia

A beautifully Julian topology optimization package.

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

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

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

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

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

Easy benchmark tracking for packages

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

Complex neural network examples for Flux.jl

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

A Julia framework for invertible neural networks

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing