A Julia machine learning framework

Koç University deep learning framework.

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

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

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/

Elegant & Performant Scientific Machine Learning in Julia

Julia bindings for the Enzyme automatic differentiator

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

MXNet Julia Package - flexible and efficient deep learning in Julia

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

A Julia package for data clustering

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

Computer vision models for Flux

A differentiable physics engine for robotics

Simple chains

Utility package for accessing common Machine Learning datasets in Julia

Graph Neural Networks in Julia

Abstract types and methods for Gaussian Processes.

Sensible extensions for exposing torch in Julia.

Reservoir computing utilities for scientific machine learning (SciML)

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

Boosted trees in Julia

Core functionality for the MLJ machine learning framework

Reverse-mode automatic differentiation in Julia

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

Julia package of loss functions for machine learning.

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Deep Learning for Julia

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

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

A flexible neural net training library inspired by fast.ai

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Julia Machine Learning library

Evaluating Robustness of Neural Networks with Mixed Integer Programming

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

Utilities and abstractions for Machine Learning tasks

Starter kit for legendary models

Explainable AI in Julia.