An Introduction to Artificial Intelligence with Julia
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Updated
Oct 5, 2024 - Jupyter Notebook
An Introduction to Artificial Intelligence with Julia
Repository housing feature selection algorithms for use with the machine learning toolbox MLJ.
Home of the MLJ model registry and tools for model queries and mode code loading
Connecting MLJ and MLFlow
An API for dispatching on the "scientific" type of data instead of the machine type
Hyperparameter optimization algorithms for use in the MLJ machine learning framework
Core functionality for the MLJ machine learning framework
A set of tutorials to show how to use Julia for data science (DataFrames, MLJ, ...)
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
MLJ.jl interface for GLM.jl models
Julia Toolkit with fairness metrics and bias mitigation algorithms
Package providing K-nearest neighbor regressors and classifiers, for use with the MLJ machine learning framework.
One package to train them all
Repository implementing MLJ interface for MultivariateStats models.
Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm
Binary Classification applying dimensionality reduction and hyperparameter tunning, working on MLJ framework in Julia. The Data comes from a Sonar System
A Least Squares Support Vector Machine implementation in pure Julia
SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
MLJ.jl interface for JLBoost.jl
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