Trends in deep learning methodologies: algorithms, applications, and systems

by Genovese, Angelo Piuri, Vincenzo Raj, Sandeep Srivastava, Rajshree

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Title Statements

Full title :Trends in deep learning methodologies : algorithms, applications, and systems / edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava.

Summary

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

Edition Statements

Publication Date:2021

Publication Statements

Publisher(s):Academic Press
Place of Publication:London, United Kingdom
Language:eng
Publication Date:2021

The Physical Object

Description:xvii, 288 pages : : illustrations (some color) ; : 23 cm

Edition Identifiers

ISBN:9780128222263, 0128222263
oclc:on1230559378
LC Classification:Q325.5
Dewey Decimal:006.3/1

Subjects

Apprentissage automatique artificial intelligence Intelligence artificielle Machine learning Neural networks (Computer science) Neural Networks, Computer Réseaux neuronaux (Informatique)