Machine-Learning Julia General-Purpose Machine Learning
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A set of functions to support the development of machine learning algorithms.A Julia framework for probabilistic graphical models.Julia package for Regularized Discriminant Analysis.Local regression, so smooooth!A Julia package for fitting (statistical) mixed-effects models.Julia module for Distance evaluation.Decision Tree Classifier and Regressor.A neural network in Julia.Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.Generalized linear models in Julia.Julia package for Gaussian processes.Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.Basic functions for clustering data: k-means, dp-means, etc.Kernel density estimators for julia.Methods for dimensionality reduction.A Julia package for non-negative matrix factorization.eXtreme Gradient Boosting Package in Julia.A Julia package for manifold learning and nonlinear dimensionality reduction.Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.Flexible Deep Learning Framework in Julia.Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.Large scale Gaussian Mixture Models.Julia implementation of the scikit-learn API.Koç University Deep Learning Framework.Relax! Flux is the ML library that doesn't make you tensorA Julia machine learning framework