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

Bayesian inference with probabilistic programming.

A Julia machine learning framework

Koç University deep 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

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

Climate Machine: an Earth System Model that automatically learns from data

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

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

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

A Curious Cumulation of CUDA Cuisine

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

Sensible extensions for exposing torch in Julia.

Reservoir computing utilities for scientific machine learning (SciML)

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

Core functionality for the MLJ machine learning framework

Reverse-mode automatic differentiation in Julia

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

A development environment for robust and global optimization

Read ONNX graphs in Julia

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