Evolutionary Deep Neural Architecture Se
Language: English
Published by Springer, 2022
- Hardcover
- New

Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
AbeBooks seller since February 27, 2001
Condition: New
US$ 275.71
Quantity: 15 available
Add to basketSeller Inventory # V9783031168673
- Title
- Evolutionary Deep Neural Architecture Se
- Author
- Sun, Yanan
- Publisher
- Springer
- Publication year
- 2022
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3031168674
- ISBN 13
- 9783031168673
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.
Kennys Bookshop and Art Galleries Ltd.
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