Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances

Yanan Sun

ISBN 10: 3031168674 / ISBN 13: 9783031168673

Editore: Springer International Publishing, Springer International Publishing Nov 2022, 2022

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This item is printed on demand - it takes 3-4 days longer - Neuware -This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields. 348 pp. Englisch. Codice articolo 9783031168673

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Titolo: Evolutionary Deep Neural Architecture Search...

Casa editrice: Springer International Publishing, Springer International Publishing Nov 2022

Data di pubblicazione: 2022

Legatura: Buch

Condizione: Neu

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Riassunto:

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.