Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances (Studies in Computational Intelligence, 1070)
Language: English
Published by Springer, 2022
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
US$ 222.67
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In English.
Seller Inventory # ria9783031168673_new
- Title
- Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances (Studies in Computational Intelligence, 1070)
- Author
- Sun, Yanan; Yen, Gary G.; Zhang, Mengjie
- Publisher
- Springer
- Publication year
- 2022
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3031168674
- ISBN 13
- 9783031168673
- Item weight
- 785 grams
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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AbeBooks seller since March 25, 2015
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