In this chapter, we have reviewed the deep neural network models that are most used in real-life applications. We started from deep feedforward network, which has a structure typical of a three-level neural network; the first layer receives the input signals, and the last returns the output signals. It is a good example of a network in which the signal flow proceeds in one direction.
Then we analyzed CNNs, which divide the input data into various overlapping fragments that are then analyzed to identify the particularities that characterize those fragments. This information is then passed on to the following layer in the form of a feature map containing the relations between neurons and particularities.
Then RNNs were addressed, which are a type of neural networks specializing in the processing of sequential data. This type of network is highly optimized for tasks related...
