Artificial Intelligence

Artificial intelligence, machine learning, and deep learning frameworks and models.

Organizations

• FluxML
• Julia AI (Alan Turing Institute)
• Julia ML

Resources

• An evaluation of Deep Learning Frameworks
• Examples from Thoughtful Machine Learning
• machine-learning-cheat-sheet : Classical equations and diagrams in machine learning by @soulmachine.
• mlpnnets.jl : Feed-forward MLP neural network implementation.
• Caltech's machine learning course by Prof. Yaser Abu-Mostafa with videos on Youtube.
• Grokking Deep Learning with Julia
• Machine Learning in Julia 2020

Machine Learning Frameworks

• alan-turing-institute/MLJ.jl : A Julia machine learning framework by The Alan Turing Institute.
• avik-pal/Lux.jl : A explicitly parameterized neural network using deeply nested named tuples.
• bcbi/PredictMD.jl : Uniform interface for machine learning in Julia. It is the official machine learning framework of the Brown Center for Biomedical Informatics.
• IBM/AutoMLPipeline.jl : a package to create complex ML pipeline structures using simple expressions.
• OML-NPA/EasyML.jl : Using machine learning in Julia through a graphical user interface.
• ppalmes/CombineML.jl : Create ensembles of machine learning models from scikit-learn, caret, and julia.

Flux

FluxML/Flux.jl : Pure Julia ML stack with lightweight abstractions on top of Julia's native GPU and AD support.

• FluxML/FastAI.jl : Repository of best practices for deep learning in Julia, inspired by fastai.
• FluxML/FluxTraining.jl : A powerful, extensible neural net training backend.
• FluxML/GeometricFlux.jl : Geometric Deep Learning for Flux.jl.
• FluxML/model-zoo : Various demonstrations of the Flux.jl machine learning library.

Knet

denizyuret/Knet.jl : Koç University deep learning framework - A machine learning module implemented in Julia.

• egeersu/KnetNLP : NLP examples in Knet.
• egeersu/KnetOnnx.jl : ONNX integration with Knet.

Bindings to libraries

• cstjean/ScikitLearn.jl : Julia implementation of the scikit-learn API via PyCall.jl. Cheatsheet.
• dmlc/XGBoost.jl : a Julia interface of XGBoost, an efficient and scalable implementation of distributed gradient boosting framework. Julia Con 2023 Video
• DrChainsaw/ONNXNaiveNASflux.jl : Import/export ONNX models for Flux.jl.
• FluxML/ONNX.jl : Read ONNX graphs and load these models in Julia.
• innerlee/LIBLINEAR.jl : Julia binding to Liblinear, a library for Large Linear Classification.
• JuliaML/LIBSVM.jl : Julia bindings for LIBSVM C library.
• jw3126/ONNXRunTime.jl : Julia bindings for the onnxruntime to perform inference.

Clustering

• Evovest/EvoTrees.jl : Boosted decision trees in Julia.
• JuliaAI/DecisionTree.jl : Julia implementation of Decision Tree (CART) and Random Forest algorithms.
• JuliaStats/Clustering.jl: Basic functions for clustering data e.g, k-means, dp-means, etc..
• KristofferC/NearestNeighbors.jl : High performance nearest neighbor data structures and algorithms for Julia.

Dataset Utilities

• JuliaML/MLDatasets.jl : Utility package for accessing common Machine Learning datasets in Julia.
• JuliaML/MLLabelUtils.jl : Utility package for working with classification targets and label-encodings.

Misc

• dfdx/Boltzmann.jl : Restricted Boltzmann Machines and Deep Belief Networks in Julia
• irhum/TopoChains.jl : A flexible data structure for multi-input multi-output models.
• JuliaGaussianProcesses/KernelFunctions.jl : Kernel functions for machine learning.
• JuliaML/LossFunctions.jl : Julia package of loss functions for machine learning. Documentation
• JuliaML/ValueHistories.jl : Utilities to efficiently track learning curves or other optimization information.
• mcreel/SimulatedNeuralMoments.jl : Bayesian and classical estimation and inference based on statistics that are filtered through a trained neural net.
• mrtzh/PrivateMultiplicativeWeights.jl : Differentially private synthetic data.
• sisl/Discretizers.jl : A package to support discretization methods and mapping functions for data discretization and label maps.
• sisl/NeuralVerification.jl : verifying whether a neural network satisfies certain input-output constraints. JuliaCon 2021 video.
• slimgroup/InvertibleNetworks.jl : Building blocks for invertible neural networks in the Julia programming language.
• trappmartin/SumProductNetworks.jl : Sum-Product Networks (deep probabilistic networks) package in Julia.
• zgornel/NetworkLearning.jl : Baseline collective classification library, including observation-based learning and entity-based learning.

Reinforcement Learning (RL)

• Wikipedia: Reinforcement Learning

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• JuliaReinforcementLearning/ReinforcementLearning.jl : A Reinforcement Learning package. See also JuliaReinforcementLearning/ReinforcementLearningAnIntroduction.jl

Natural language processing (NLP)

• Wikipedia: Natural language processing
• JuliaText organization

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• andrewcooke/ParserCombinator.jl : A parser combinator library.
• dellison/DependencyTrees.jl : A package for dependency parsing.
• domluna/GloVe.jl : Implements Global Word Vectors.
• JuliaNeighbors/BKTrees.jl : Julia implementation of Burkhard-Keller trees.
• JuliaText/CorpusLoaders.jl : A variety of loaders for various NLP corpora.
• JuliaText/Languages.jl : A package for working with human languages.
• JuliaText/TextAnalysis.jl : A Julia package for text analysis.
• JuliaText/Word2Vec.jl : Julia interface to word2vec.
• JuliaText/WordNet.jl : A Julia package for Princeton's WordNet®.
• sbos/AdaGram.jl : Adaptive Skip-gram implementation in Julia.
• slycoder/TopicModels.jl : Topic models are Bayesian, hierarchical mixture models of discrete data.
• zgornel/ConceptnetNumberbatch.jl : Julia interface to ConceptnetNumberbatch.
• zgornel/Glowe.jl : Julia interface to Global Word Vectors.
• zgornel/StringAnalysis.jl : A hard fork of the TextAnalysis.jl package, designed to provide a richer, faster and orthogonal API.

English

• TorkelE/Why.jl : A simple function, why(), which gives randomly generated answers.
• TotalVerb/EnglishText.jl : Utilities for English-language quirks in Julia.

Speech recognition

• idiap/TIDIGITSRecipe.jl : A Julia recipe for training an ASR system using the TIDIGITS database.
• JuliaDSP/MFCC.jl : Standard Mel Frequency Cepstral Coefficients feature extraction for speech analysis.
• r9y9/WORLD.jl : A Julia wrapper for WORLD - a high-quality speech analysis, modification and synthesis system.

Spiking neural network

Wikipedia: Spiking neural network

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• AStupidBear/SpikingNeuralNetworks.jl : Julia Spiking Neural Network Simulator.
• Dhruva2/NeuronBuilder.jl : building small networks of detailed, conductance-based neurons out of ion channels and synapses.
• JuliaNeuroscience/SpikeSynchrony.jl : Measuring distances, synchrony and correlation between spike trains.
• leaflabs/WaspNet.jl : fixed-time-step simulations of primarily spiking neural networks (SNNs).
• wsphillips/Conductor.jl : a Julia-based neuronal network simulator engine.