Machine Learning Packages
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MLJ.jl1589A Julia machine learning framework
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Knet.jl1403Koç University deep learning framework.
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BrainFlow.jl935BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
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TensorFlow.jl866A Julia wrapper for TensorFlow
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DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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FastAI.jl557Repository of best practices for deep learning in Julia, inspired by fastai
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ScikitLearn.jl520Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
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MXNet.jl371MXNet Julia Package - flexible and efficient deep learning in Julia
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StatisticalRethinking.jl366Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.
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AutoMLPipeline.jl325A package that makes it trivial to create and evaluate machine learning pipeline architectures.
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DecisionTree.jl316Julia implementation of Decision Tree (CART) and Random Forest algorithms
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Clustering.jl311A Julia package for data clustering
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Enzyme.jl311Julia bindings for the Enzyme automatic differentiator
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Lux.jl300Explicitly Parameterized Neural Networks in Julia
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Metalhead.jl297Computer vision models for Flux
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Dojo.jl221A differentiable physics engine for robotics
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MLDatasets.jl204Utility package for accessing common Machine Learning datasets in Julia
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SimpleChains.jl195Simple chains
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AbstractGPs.jl192Abstract types and methods for Gaussian Processes.
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Torch.jl183Sensible extensions for exposing torch in Julia.
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MLBase.jl179A set of functions to support the development of machine learning algorithms
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ReservoirComputing.jl172Reservoir computing utilities for scientific machine learning (SciML)
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GraphNeuralNetworks.jl153Graph Neural Networks in Julia
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Yota.jl145Reverse-mode automatic differentiation in Julia
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Merlin.jl144Deep Learning for Julia
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EvoTrees.jl143Boosted trees in Julia
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MLJBase.jl140Core functionality for the MLJ machine learning framework
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LossFunctions.jl137Julia package of loss functions for machine learning.
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ForneyLab.jl135Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
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AugmentedGaussianProcesses.jl132Gaussian Process package based on data augmentation, sparsity and natural gradients
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MLJFlux.jl115Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox
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MachineLearning.jl113Julia Machine Learning library
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Avalon.jl105Starter kit for legendary models
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MIPVerify.jl104Evaluating Robustness of Neural Networks with Mixed Integer Programming
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MLDataUtils.jl102Utility package for generating, loading, splitting, and processing Machine Learning datasets
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FluxTraining.jl95A flexible neural net training library inspired by fast.ai
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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
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TSML.jl94A package for time series data processing, classification, clustering, and prediction.
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GLMNet.jl89Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
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TensorBoardLogger.jl88Easy peasy logging to TensorBoard with Julia
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