MLJ.jl - A Julia machine learning framework

•    Jupyter

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New to MLJ? Start here. Wanting to integrate an existing machine learning model into the MLJ framework? Start here.

data-science machine-learning statistics pipeline clustering julia pipelines regression tuning classification ensemble-learning predictive-modeling tuning-parameters stacking

hypersearch - Hyerparameter Optimization for PyTorch

•    Python

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Tune the hyperparameters of your PyTorch models with HyperSearch. Keys are of the form {layer_num}_{hyperparameter} where layer_num can be a layer from your nn.Sequential model or all to signify all layers. Values are of the form [distribution, x] where distribution can be one of uniform, quniform, choice, etc.

hyperband hyperparameter-optimization pytorch deep-learning tuning-parameters

mlrHyperopt - Easy Hyper Parameter Optimization with mlr and mlrMBO.

•    HTML

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Easy Hyper Parameter Optimization with mlr and mlrMBO. Mainly it uses the learner implemented in mlr and uses the tuning methods also available in mlr. Unfortunately mlr lacks of well defined search spaces for each learner to make hyperparameter tuning easy.

mlr optimization learners hyperparameter-optimization tuning-parameters r-package r machine-learning