Training Supervised Deep Learning Networks

Abstract

Training supervised deep learning networks involves obtaining model parameters using labeled dataset to allow the network to map an input data to a class label. The labeled dataset consists of training examples, where each example is a pair of an input data and a desired class label. The deep model parameters allow the network to correctly determine the class labels for unseen instances. This requires the model to generalize from the training dataset to unseen instances.