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

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.

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

Julia Implementation of Transformer models

Geometric Deep Learning for Flux

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

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

Graph Neural Networks 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

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

Package for Simulation, Tomography and Analysis of Quantum Computers

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

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

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.

MagNav: airborne Magnetic anomaly Navigation

3D computer vision library in Julia

Pure Julia implementations of single-pass object detection neural networks.

Build flexible hierarchical multi-instance learning models.

Package implementing common state-space routines.

OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.

High level functions for analyzing the output of simulations

Implementation of variational Bayes inference algorithms

Implementation of the Deep Q-learning algorithm to solve MDPs

The amazing Reaction Mechanism Simulator for simulating large chemical kinetic mechanisms