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Deep Learning: Fundamentals, Theory and Applications: 2 - Rilegato

 
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VII, 163 p. Hardcover. Versand...
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Huang, Kaizhu; et al. (Eds.)
Editore: Cham, Springer., 2019
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania

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VII, 163 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Cognitive Computation Trends, Volume 2. Sprache: Englisch. Codice articolo 34891AB

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Kaizhu Huang
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Gebundene Ausgabe. Condizione: Gut. Gebraucht - Gut GU - Beschädigungen, Verschmutzungen, ungelesenes Mängelexemplar, gestempelt, Versand Büchersendung - The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications. Codice articolo INF1000495922

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Kaizhu Huang
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications. 172 pp. Englisch. Codice articolo 9783030060725

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Huang, Kaizhu|Hussain, Amir|Wang, Qiu-Feng|Zhang, Rui
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides thorough&nbspbackground of deep learningIntroduces &nbspwidely-used learning architectures and algorithmsIncludes new theory and applications of deep learning. Codice articolo 256091318

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Kaizhu Huang
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications. Codice articolo 9783030060725

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Editore: Springer, 2019
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.

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Condizione: New. Codice articolo ABLING22Oct2018170258202

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Kaizhu Huang
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Da: Grand Eagle Retail, Fairfield, OH, U.S.A.

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Hardcover. Condizione: new. Hardcover. The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9783030060725

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Huang, Kaizhu (Edited by)/ Hussain, Amir (Edited by)/ Wang, Qiu-Feng (Edited by)/ Zhang, Rui (Edited by)
Editore: Springer, 2019
ISBN 10: 3030060721 ISBN 13: 9783030060725
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Hardcover. Condizione: Brand New. 163 pages. 9.25x6.10x0.47 inches. In Stock. Codice articolo x-3030060721

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