Kirjojen hintavertailu – 12 903 725 kirjaa ja 27 kauppaa

Kirjailija

Gary G. Yen

Kirjat ja teokset yhdessä paikassa: 2 kirjaa, julkaisuja vuosilta 2022–2023, suosituimpiin kuuluu Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.

2 kirjaa

Kirjojen julkaisuvuodet: 2022–2023.

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

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

Yanan Sun; Gary G. Yen; Mengjie Zhang

Springer International Publishing AG
2023
nidottu
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.
Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances

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

Yanan Sun; Gary G. Yen; Mengjie Zhang

Springer International Publishing AG
2022
sidottu
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.