Neural networks and deep learning

A. Pastor López-Monroy; Jesus Salvador Garcia Salinas

doi:10.1016/b978-0-12-820125-1.00021-x

Neural networks and deep learning

Abstract

In this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings for different scenarios and variants of CNNs. Finally, the third part presents Neural Networks for sequence modeling, in particular Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) and Attention Mechanisms. The description of the latter models are made in the context of different applications that allows to explain in a better way the details of each particular kind of neural network.

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Authors (2)

A. Pastor López-Monroy
Jesus Salvador Garcia Salinas (formerly: Jesus Garcia Salinas) dr

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Category:: Monographic publication
Type:: rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:: Biosignal Processing and Classification Using Computational Learning and Intelligence strony 177 - 196
Language:: English
Publication year:: 2022
Bibliographic description:: López-Monroy A. P., Garcia Salinas J.: Neural networks and deep learning// Biosignal Processing and Classification Using Computational Learning and Intelligence/ : , 2022, s.177-196
DOI:: Digital Object Identifier (open in new tab) 10.1016/b978-0-12-820125-1.00021-x
Verified by:: Gdańsk University of Technology