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

Play atmospheric modelling like it's LEGO.

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

Molecular simulation in Julia

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.

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.

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

A package for time series data processing, classification, clustering, and prediction.

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Utilities and abstractions for Machine Learning tasks

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.

Julia Package for MRI Reconstruction

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