Artificial Intelligence (AI)	a branch of computer science in which the machines attempt to emulate what the human intelli-gence might do in the same situation.
Machine Learning (ML)	a branch of AI where, to emulate human predictions, machines are fed with data and learn by them.
Deep learning (DL)	a type of ML based on the use of artificial representation of human neural architecture, known as artificial neural networks.
Neural Network (NN)	an artificial representation of human neural architecture, characterized by the presence several layers, including an input and an output separated by several hidden layers.
Convolutional Neural Network (CNN)	a DL model characterized by the presence of several convolutional layers which are not fully connected each other but only with certain part of specific layers; they are mostly used to aggre-gate information for a global prediction.
Fully Convolutional Network (FCN)	a DL model characterized by a hierarchical communication among different layers; they are used to highlight information obtained from each pixel.
Recurrent Neural Network (RNN)	a DL model which, unlike CNN and FCN, can take into account inputs at different time points; they are used to learn task which might benefit from a dynamic contribution of information.
Generative Adversarial Networks (GAN)	a DL model which use simultaneously two NN: the first generating data from input, the second checking the agreement between original and generated data; they are used to decrease the degree of mistakes.