Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances (Studies in Computational Intelligence) by Sun, Yanan; Yen, Gary G.; Zhang, Mengjie: New (2023)

About this Item

Seller Inventory # I-9783031168703

Bibliographic Details

Title: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances (Studies in Computational Intelligence)
Publisher: Springer
Publication Date: 2023
Language: English
ISBN 10: 3031168704
ISBN 13: 9783031168703
Binding: Soft cover
Condition: New

About this title

Synopsis

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.

From the Back Cover

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.

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