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Trends Deep Learning Methodologies (13 results)
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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems (Hybrid Computational Intelligence for Pattern Analysis and Understanding)
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 137.06
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Trends in Deep Learning Methodologies : Algorithms, Applications, and Systems
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 139.56
£ 15 shipping
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Condition: As New. Unread book in perfect condition.
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Taschenbuch. Condition: Neu. Trends in Deep Learning Methodologies | Algorithms, Applications, and Systems | Vincenzo Piuri (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Academic Press | EAN 9780128222263 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Trends in Deep Learning Methodologies : Algorithms, Applications, and Systems
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 155.15
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Trends in Deep Learning Methodologies
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingLanguage: English
Published by Elsevier Science & Technology, Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Englisch.
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Paperback. Condition: Brand New. 288 pages. 8.75x6.00x0.75 inches. In Stock. This item is printed on demand.
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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingLanguage: English
Published by Elsevier Science & Technology|Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep gener.
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Trends in Deep Learning Methodologies : Algorithms, Applications, and Systems
Book 3 of 3: Hybrid Computational Intelligence for Pattern Analysis and UnderstandingLanguage: English
Published by Elsevier Science & Technology, Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
