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
• Natural Language Processing
• Data Analysis / Data Visualization
• Misc Stuff / Presentations

APL

• Julia – high-level, high-performance dynamic programming language for technical computing
• IJulia – a Julia-language backend combined with the Jupyter interactive environment

General-Purpose Machine Learning

• MachineLearning - Julia Machine Learning library.
• MLBase - A set of functions to support the development of machine learning algorithms.
• PGM - A Julia framework for probabilistic graphical models.
• DA - Julia package for Regularized Discriminant Analysis.
• Regression - Algorithms for regression analysis (e.g. linear regression and logistic regression).
• Local Regression - Local regression, so smooooth!.
• Naive Bayes - Simple Naive Bayes implementation in Julia.
• Mixed Models - A Julia package for fitting (statistical) mixed-effects models.
• Simple MCMC - basic mcmc sampler implemented in Julia.
• Distance - Julia module for Distance evaluation.
• Decision Tree - Decision Tree Classifier and Regressor.
• Neural - A neural network in Julia.
• MCMC - MCMC tools for Julia.
• Mamba - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.
• GLM - Generalized linear models in Julia.
• Gaussian Processes - Julia package for Gaussian processes.
• Online Learning
• GLMNet - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.
• Clustering - Basic functions for clustering data: k-means, dp-means, etc.
• SVM - SVM's for Julia.
• Kernel Density - Kernel density estimators for julia.
• Dimensionality Reduction - Methods for dimensionality reduction.
• NMF - A Julia package for non-negative matrix factorization.
• ANN - Julia artificial neural networks.
• Mocha - Deep Learning framework for Julia inspired by Caffe.
• XGBoost - eXtreme Gradient Boosting Package in Julia.
• ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction.
• MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
• Merlin - Flexible Deep Learning Framework in Julia.
• ROCAnalysis - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.
• GaussianMixtures - Large scale Gaussian Mixture Models.
• ScikitLearn - Julia implementation of the scikit-learn API.
• Knet - Koç University Deep Learning Framework.

Natural Language Processing

• Topic Models - TopicModels for Julia.
• Text Analysis - Julia package for text analysis.

Data Analysis / Data Visualization

• Graph Layout - Graph layout algorithms in pure Julia.
• LightGraphs - Graph modeling and analysis.
• Data Frames Meta - Metaprogramming tools for DataFrames.
• Julia Data - library for working with tabular data in Julia.
• Data Read - Read files from Stata, SAS, and SPSS.
• Hypothesis Tests - Hypothesis tests for Julia.
• Gadfly - Crafty statistical graphics for Julia.
• Stats - Statistical tests for Julia.
• RDataSets - Julia package for loading many of the data sets available in R.
• DataFrames - library for working with tabular data in Julia.
• Distributions - A Julia package for probability distributions and associated functions.
• Data Arrays - Data structures that allow missing values.
• Time Series - Time series toolkit for Julia.
• Sampling - Basic sampling algorithms for Julia.

Misc Stuff / Presentations

• DSP - Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
• JuliaCon Presentations - Presentations for JuliaCon.
• SignalProcessing - Signal Processing tools for Julia.
• Images - An image library for Julia.