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

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

A Julia wrapper for TensorFlow

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

Julia Implementation of Transformer models

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

Geometric Deep Learning for Flux

Computer vision models for Flux

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

Utility package for accessing common Machine Learning datasets 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.

Causal, Higher-Order, Probabilistic Programming

Core functionality for the MLJ machine learning framework

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

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.

A flexible neural net training library inspired by fast.ai

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

3D computer vision library in Julia

MagNav: airborne Magnetic anomaly Navigation

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

Build flexible hierarchical multi-instance learning models.

Fast, scalable and flexible Outlier Detection with Julia

Implementation of the Deep Q-learning algorithm to solve MDPs

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Global glacier model using Universal Differential Equations for climate-glacier interactions

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Visualization for Flux.Chain neural networks

Atomic graph models for molecules and crystals in Julia

Use Optim to train Flux models and visualize loss landscapes

Distributed Data Parallel Training of Deep Neural Networks

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.