Seller: Majestic Books, Hounslow, United Kingdom
US$ 154.24
Quantity: 3 available
Add to basketCondition: New. pp. 270.
Condition: New. pp. 270.
Condition: New.
Language: English
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
US$ 174.87
Quantity: Over 20 available
Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
Condition: New. pp. 270.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
US$ 184.56
Quantity: Over 20 available
Add to basketCondition: New.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
US$ 195.45
Quantity: Over 20 available
Add to basketCondition: New. In.
Condition: As New. Unread book in perfect condition.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
US$ 217.43
Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Condition: new. Questo è un articolo print on demand.
Language: English
Published by Elsevier Science & Technology, Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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.
Seller: Revaluation Books, Exeter, United Kingdom
US$ 160.50
Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 288 pages. 8.75x6.00x0.75 inches. In Stock. This item is printed on demand.
Language: English
Published by Elsevier Science & Technology|Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Seller: moluna, Greven, Germany
US$ 158.22
Quantity: Over 20 available
Add to basketCondition: 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.
Language: English
Published by Elsevier Science & Technology, Academic Press, 2020
ISBN 10: 0128222263 ISBN 13: 9780128222263
Seller: AHA-BUCH GmbH, Einbeck, Germany
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.