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

A Julia machine learning framework

Koç University deep learning framework.

Deep Learning framework for Julia

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

A Julia wrapper for TensorFlow

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

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

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

Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/

A reinforcement learning package for Julia

Julia Implementation of Transformer models

MXNet Julia Package - flexible and efficient deep learning in Julia

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Julia package for text analysis

A package that makes it trivial to create and evaluate machine learning pipeline architectures.

Julia implementation of Decision Tree (CART) and Random Forest algorithms

Julia bindings for the Enzyme automatic differentiator

A Julia package for data clustering

Explicitly Parameterized Neural Networks in Julia

Computer vision models for Flux

A differentiable physics engine for robotics

Utility package for accessing common Machine Learning datasets in Julia

Abstractions, algorithms, and utilities for reinforcement learning in Julia

Simple chains

Abstract types and methods for Gaussian Processes.

Sensible extensions for exposing torch in Julia.

A set of functions to support the development of machine learning algorithms

Reservoir computing utilities for scientific machine learning (SciML)

Adaptive Skip-gram implementation in Julia

Neural Network primitives with multiple backends

Graph Neural Networks in Julia

Reverse-mode automatic differentiation in Julia

Deep Learning for Julia

Boosted trees in Julia

Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.

Core functionality for the MLJ machine learning framework

Julia package of loss functions for machine learning.

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.