Machine Learning Packages

MLJ.jl1589A Julia machine learning framework

Knet.jl1403Koç University deep learning framework.

BrainFlow.jl935BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

TensorFlow.jl866A Julia wrapper for TensorFlow

DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+nonstiff DE solvers, demonstrating scientific machine learning (SciML) and physicsinformed machine learning methods

FastAI.jl557Repository of best practices for deep learning in Julia, inspired by fastai

ScikitLearn.jl520Julia implementation of the scikitlearn API https://cstjean.github.io/ScikitLearn.jl/dev/

MXNet.jl371MXNet Julia Package  flexible and efficient deep learning in Julia

StatisticalRethinking.jl366Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

AutoMLPipeline.jl325A package that makes it trivial to create and evaluate machine learning pipeline architectures.

DecisionTree.jl316Julia implementation of Decision Tree (CART) and Random Forest algorithms

Clustering.jl311A Julia package for data clustering

Enzyme.jl311Julia bindings for the Enzyme automatic differentiator

Lux.jl300Explicitly Parameterized Neural Networks in Julia

Metalhead.jl297Computer vision models for Flux

Dojo.jl221A differentiable physics engine for robotics

MLDatasets.jl204Utility package for accessing common Machine Learning datasets in Julia

SimpleChains.jl195Simple chains

AbstractGPs.jl192Abstract types and methods for Gaussian Processes.

Torch.jl183Sensible extensions for exposing torch in Julia.

MLBase.jl179A set of functions to support the development of machine learning algorithms

ReservoirComputing.jl172Reservoir computing utilities for scientific machine learning (SciML)

GraphNeuralNetworks.jl153Graph Neural Networks in Julia

Yota.jl145Reversemode automatic differentiation in Julia

Merlin.jl144Deep Learning for Julia

EvoTrees.jl143Boosted trees in Julia

MLJBase.jl140Core functionality for the MLJ machine learning framework

LossFunctions.jl137Julia package of loss functions for machine learning.

ForneyLab.jl135Julia package for automatically generating Bayesian inference algorithms through message passing on Forneystyle factor graphs.

AugmentedGaussianProcesses.jl132Gaussian Process package based on data augmentation, sparsity and natural gradients

MLJFlux.jl115Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

MachineLearning.jl113Julia Machine Learning library

Avalon.jl105Starter kit for legendary models

MIPVerify.jl104Evaluating Robustness of Neural Networks with Mixed Integer Programming

MLDataUtils.jl102Utility package for generating, loading, splitting, and processing Machine Learning datasets

FluxTraining.jl95A flexible neural net training library inspired by fast.ai

ZigZagBoomerang.jl95Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

TSML.jl94A package for time series data processing, classification, clustering, and prediction.

GLMNet.jl89Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet

TensorBoardLogger.jl88Easy peasy logging to TensorBoard with Julia
Loading more...