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First we define the model configurations that we want to train, meaning either model architectures or hyperparameters, and load them into a model selection table.  In the picture below there are three model configurations represented by the three different purple shapes:

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The epochs parameter is discussed later on this page.

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Once we have model combinations in the model selection table, we call the fit function to train the models in parallel.  In the picture below, the three orange shapes represent the three models that have been trained:

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The number_of_iterations is discussed later on this page.

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