This node performs supervised training of a feedforward deep learning model for regression. Thereby, the learning procedure can be adjusted using several training methods and parameters, which can be customized in the node dialog. Additionally, the node supplies further methods for regularization, gradient normalization and learning refinements. The learner node automatically adds an output layer to the network configuration, which can be also configured in the node dialog. For regression, the output layer will always use 'identity' as the activation function and the number of outputs will be automatically set to match the number target values. The output of the node is a trained deep learning model which can be used to predict target values.
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