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

Koç University deep learning framework.

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

🌊 Fast and friendly fluid dynamics on CPUs and GPUs

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

Julia package for tensor contractions and related operations

Geometric Deep Learning for Flux

Computer vision models for Flux

Julia Implementation of Transformer models

Local, global, and beyond optimization for scientific machine learning (SciML)

Surrogate modeling and optimization for scientific machine learning (SciML)

Differentiable RayTracing in Julia

Molecular simulation in Julia

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

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains

Causal, Higher-Order, Probabilistic Programming

Reverse-mode automatic differentiation in Julia

Read ONNX graphs in Julia

One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD

An interface to the deep learning package Flux.jl from the MLJ.jl toolbox

Geophysical fluid dynamics pseudospectral solvers with Julia and FourierFlows.jl.

Boosted trees in Julia

A Julia package for large-scale tensor computations, with a hint of category theory

Starter kit for legendary models

Probabilistic Circuits from the Juice library

Algorithms that combine tensor network methods with automatic differentiation

A Julia package for numerical computation in quantum information theory

3D computer vision library in Julia

Multiple Instance Learning Library is build on top of Flux.jl aimed to prototype flexible multi-instance learning models.

Renamed to Avalon.jl

MIRT: Michigan Image Reconstruction Toolbox (Julia version)

Implementation of the Deep Q-learning algorithm to solve MDPs

Universal modeling and simulation of fluid dynamics upon machine learning

A workbench for Quantum Chemistry and Quantum Physics in Julia

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

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