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Convolutional neural network (CNN) [14] is an example of a various number of Deep Learning models.

The deployment of deep learning models for big data reduction is potential research direction that can be pursued in future.

Furthermore, features extracted by deep learning model show extraordinary performance over overwhelming majorities of existing hand-crafted features [64 66].

Each layer of a deep learning model lets the computer identify another level of abstraction of the same object.

This shows the potential of the deep learning model for the application of modulation classification.

The typical architecture of the DBN in Fig. 2 shows the multilayer representation of the deep learning model.

The company has published the methodology of their deep learning model on arXiv.

Further, entities that are behind emotions could be gotten with the help of SENNA deep learning model.

Next, the convergence property of the hybrid deep learning model is analyzed.

As the millions of parameters in the deep learning model, how to reduce the parameter number and compress the model is another research topic to accelerate the deep neural network [34 38].

A typical deep learning approach consists of three phases: (i) training a deep learning model from training data with pre-defined labels; (ii) pass the images through the trained model to extract the feature representations; and finally (iii) applying fully connection layers of the deep architecture or other models such as K-nearest neighbor (KNN) to obtain the best match images.

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