"NeuralNetwork" (Machine Learning Method)

Details & Suboptions

  • A neural network consists of stacked layers, each performing a simple computation. Information is processed layer by layer from the input layer to the output layer. The neural network is trained to minimize a loss function on the training set using gradient descent.
  • The following options can be given:
  • MaxTrainingRoundsAutomaticmaximum number of iterations over the dataset
    "NetworkDepth"Automaticthe depth of the network
  • The option "NetworkDepth" controls the capacity of the network. A deeper network will be able to fit more complex patterns but will be more prone to overfitting.

    The option MaxTrainingRounds can be used to speed up the training but also as a regularization parameter: setting a lower value can prevent overfitting.

Examples

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Basic Examples  (2)

Train a classifier function on labeled examples:

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Obtain information about the classifier:

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Classify a new example:

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Generate some data and visualize it:

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Train a predictor function on it:

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Compare the data with the predicted values and look at the standard deviation:

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Options  (2)