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https://github.com/widged/awesome-julia-datasciences

Resources about Julia for DataSciences / Machine Learning
https://github.com/widged/awesome-julia-datasciences

List: awesome-julia-datasciences

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Resources about Julia for DataSciences / Machine Learning

Host: GitHub
URL: https://github.com/widged/awesome-julia-datasciences
Owner: widged
License: cc0-1.0
Created: 2018-10-25T10:12:11.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2018-10-25T10:55:01.000Z (about 6 years ago)
Last Synced: 2024-04-29T18:16:27.244Z (6 months ago)
Size: 19.5 KB
Stars: 14
Watchers: 2
Forks: 2
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

ultimate-awesome - awesome-julia-datasciences - Resources about Julia for DataSciences / Machine Learning. (Programming Language Lists / Julia Lists)

README

        # awesome-julia-datasciences

Resources about Julia for DataSciences / Machine Learning

I really find it easier to maintain bookmarks in a datasheet format:

https://airtable.com/invite/l?inviteId=invleOL88hv5OVxC0&inviteToken=29040a8d2357b8bc7a83c66a48729e99e7037ea558cd41185dc92d5f0416f2d1

If you want to help maintain that list, simply ask. 

Below is a copy and paste of the Julia resources found in other awesome lists. My own list includes these and others in an airtable file. I will convert them to an awesome format from time to time.

## Table of Contents

- [General-Purpose Machine Learning](#julia-general-purpose)

- [Natural Language Processing](#julia-nlp)

- [Data Analysis / Data Visualization](#julia-data-analysis)

- [Misc Stuff / Presentations](#julia-misc)



## APL

 * [Julia](http://julialang.org) – high-level, high-performance dynamic programming language for technical computing

 * [IJulia](https://github.com/JuliaLang/IJulia.jl) – a Julia-language backend combined with the Jupyter interactive environment



### General-Purpose Machine Learning

* [MachineLearning](https://github.com/benhamner/MachineLearning.jl) - Julia Machine Learning library.

* [MLBase](https://github.com/JuliaStats/MLBase.jl) - A set of functions to support the development of machine learning algorithms.

* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.

* [DA](https://github.com/trthatcher/DiscriminantAnalysis.jl) - Julia package for Regularized Discriminant Analysis.

* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression).

* [Local Regression](https://github.com/JuliaStats/Loess.jl) - Local regression, so smooooth!.

* [Naive Bayes](https://github.com/nutsiepully/NaiveBayes.jl) - Simple Naive Bayes implementation in Julia.

* [Mixed Models](https://github.com/dmbates/MixedModels.jl) - A Julia package for fitting (statistical) mixed-effects models.

* [Simple MCMC](https://github.com/fredo-dedup/SimpleMCMC.jl) - basic mcmc sampler implemented in Julia.

* [Distance](https://github.com/JuliaStats/Distance.jl) - Julia module for Distance evaluation.

* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor.

* [Neural](https://github.com/compressed/BackpropNeuralNet.jl) - A neural network in Julia.

* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia.

* [Mamba](https://github.com/brian-j-smith/Mamba.jl) - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.

* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia.

* [Gaussian Processes](https://github.com/STOR-i/GaussianProcesses.jl) - Julia package for Gaussian processes.

* [Online Learning](https://github.com/lendle/OnlineLearning.jl)

* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.

* [Clustering](https://github.com/JuliaStats/Clustering.jl) - Basic functions for clustering data: k-means, dp-means, etc.

* [SVM](https://github.com/JuliaStats/SVM.jl) - SVM's for Julia.

* [Kernel Density](https://github.com/JuliaStats/KernelDensity.jl) - Kernel density estimators for julia.

* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction.

* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization.

* [ANN](https://github.com/EricChiang/ANN.jl) - Julia artificial neural networks.

* [Mocha](https://github.com/pluskid/Mocha.jl) - Deep Learning framework for Julia inspired by Caffe.

* [XGBoost](https://github.com/dmlc/XGBoost.jl) - eXtreme Gradient Boosting Package in Julia.

* [ManifoldLearning](https://github.com/wildart/ManifoldLearning.jl) - A Julia package for manifold learning and nonlinear dimensionality reduction.

* [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.

* [Merlin](https://github.com/hshindo/Merlin.jl) - Flexible Deep Learning Framework in Julia.

* [ROCAnalysis](https://github.com/davidavdav/ROCAnalysis.jl) - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.

* [GaussianMixtures](https://github.com/davidavdav/GaussianMixtures.jl) - Large scale Gaussian Mixture Models.

* [ScikitLearn](https://github.com/cstjean/ScikitLearn.jl) - Julia implementation of the scikit-learn API.

* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.



#### Natural Language Processing

* [Topic Models](https://github.com/slycoder/TopicModels.jl) - TopicModels for Julia.

* [Text Analysis](https://github.com/johnmyleswhite/TextAnalysis.jl) - Julia package for text analysis.



### Data Analysis / Data Visualization

* [Graph Layout](https://github.com/IainNZ/GraphLayout.jl) - Graph layout algorithms in pure Julia.

* [LightGraphs](https://github.com/JuliaGraphs/LightGraphs.jl) - Graph modeling and analysis.

* [Data Frames Meta](https://github.com/JuliaStats/DataFramesMeta.jl) - Metaprogramming tools for DataFrames.

* [Julia Data](https://github.com/nfoti/JuliaData) - library for working with tabular data in Julia.

* [Data Read](https://github.com/WizardMac/ReadStat.jl) - Read files from Stata, SAS, and SPSS.

* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia.

* [Gadfly](https://github.com/GiovineItalia/Gadfly.jl) - Crafty statistical graphics for Julia.

* [Stats](https://github.com/JuliaStats/Stats.jl) - Statistical tests for Julia.

* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R.

* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia.

* [Distributions](https://github.com/JuliaStats/Distributions.jl) - A Julia package for probability distributions and associated functions.

* [Data Arrays](https://github.com/JuliaStats/DataArrays.jl) - Data structures that allow missing values.

* [Time Series](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia.

* [Sampling](https://github.com/lindahua/Sampling.jl) - Basic sampling algorithms for Julia.



### Misc Stuff / Presentations

* [DSP](https://github.com/JuliaDSP/DSP.jl) - Digital Signal Processing (filtering, periodograms, spectrograms, window functions).

* [JuliaCon Presentations](https://github.com/JuliaCon/presentations) - Presentations for JuliaCon.

* [SignalProcessing](https://github.com/davidavdav/SignalProcessing.jl) - Signal Processing tools for Julia.

* [Images](https://github.com/JuliaImages/Images.jl) - An image library for Julia.