Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Add to basketHardcover. Condition: Brand New. 484 pages. 10.00x7.00x1.25 inches. In Stock.
Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Cambridge University Press, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Published by Springer Nature Singapore, 2022
ISBN 10: 9811616833 ISBN 13: 9789811616839
Language: English
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 468 | Sprache: Englisch | Produktart: Bücher.
Published by Cambridge University Press Jan 2023, 2023
ISBN 10: 1108835767 ISBN 13: 9781108835763
Language: English
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Add to basketBuch. Condition: Neu. Neuware - 'This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component'--.
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Published by Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811616833 ISBN 13: 9789811616839
Language: English
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
Published by Springer Nature Singapore, Springer Nature Singapore, 2021
ISBN 10: 9811616809 ISBN 13: 9789811616808
Language: English
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
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Add to basketHardcover. Condition: Brand New. 464 pages. 9.50x6.50x1.00 inches. In Stock.
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides insights for researchers to minimize the research gap in machine/deep learningIncludes outbreak research on Deep Learning Offers latest tools and techniques for multimedia data analysisComprises of recent deep learn.