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Concepts and Real-Time Applications of Deep Learning / edited by Smriti Srivastava, Manju Khari, Ruben Gonzalez Crespo, Gopal Chaudhary, Parul Arora.

Contributor(s): Srivastava, Smriti, editor literario | Khari, Manju, editor literario | González Crespo, Rubén A., editor literario | Chaudhary, Gopal, editor literario | Arora, Parul, editor literario
Series: (EAI/Springer Innovations in Communication and Computing, 2522-8609); (Intelligent Technologies and Robotics (SpringerNature-42732)); (Intelligent Technologies and Robotics (R0) (SpringerNature-43728)).Publisher: Cham : Springer International Pulishing, 2021Edition: First edition 2021.Description: 1 recurso en línea (X, 209 páginas) : 86 ilustraciones, 74 ilustraciones a color.ISBN: 9783030761677.Subject: Aprendizaje automáticoOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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.
Abstract: 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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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q325.5 2021 EB (Browse shelf(Opens below)) Acceso electrónico eBook.23122121
Total holds: 0

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.

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