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Concepts and real-time applications ...

Srivastava, Smriti.

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Concepts and real-time applications of deep learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Concepts and real-time applications of deep learning/ edited by Smriti Srivastava ... [et al.].
其他作者:	Srivastava, Smriti.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	x, 209 p. :ill., digital ;24 cm.
內容註:	Introduction -- AI Techniques based on Modern Deep Learning architectures -- Application of Deep learning in Optimization and Regression -- Deep learning in Pattern recognition -- Application of Deep Learning in Healthcare systems for diagnosing disease -- Application of Deep Learning for Security and threats -- Deep Learning Framework and Tools -- Advancements in Deep Learning -- Multidisciplinary Applications of Machine/Deep Learning -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-76167-7
ISBN:	9783030761677

Concepts and real-time applications of deep learning
Concepts and real-time applications of deep learning[electronic resource] /edited by Smriti Srivastava ... [et al.]. - Cham :Springer International Publishing :2021. - x, 209 p. :ill., digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8609. - EAI/Springer innovations in communication and computing..

Introduction -- AI Techniques based on Modern Deep Learning architectures -- Application of Deep learning in Optimization and Regression -- Deep learning in Pattern recognition -- Application of Deep Learning in Healthcare systems for diagnosing disease -- Application of Deep Learning for Security and threats -- Deep Learning Framework and Tools -- Advancements in Deep Learning -- Multidisciplinary Applications of Machine/Deep Learning -- Conclusion.

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

ISBN: 9783030761677

Standard No.: 10.1007/978-3-030-76167-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .C66 2021

Dewey Class. No.: 006.31

Concepts and real-time applications of deep learning
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520 $a This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.
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