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Condition: New. pp. 100 1st ed. 2018 edition NO-PA16APR2015-KAP.
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Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)
Book 256 of 322: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 82.00
US$ 16.01 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Deep Neural Networks in a Mathematical Framework
Book 256 of 322: SpringerBriefs in Computer ScienceUS$ 79.52
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Ships from United Kingdom to U.S.A.Quantity: 10 available
Add to basketPF. Condition: New.
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Deep Neural Networks in a Mathematical Framework
Book 256 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer International Publishing, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Condition: New.
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Deep Neural Networks in a Mathematical Framework
Book 256 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer-Verlag New York Inc, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
US$ 126.28
US$ 13.36 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 84 pages. 9.00x6.00x0.25 inches. In Stock.
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Taschenbuch. Condition: Neu. Deep Neural Networks in a Mathematical Framework | Anthony L. Caterini (u. a.) | Taschenbuch | xiii | Englisch | 2018 | Springer | EAN 9783319753034 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Deep Neural Networks in a Mathematical Framework
Book 256 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Berlin, Springer, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but alsoto those outside of the neutral network community.
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Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)
Book 256 of 322: SpringerBriefs in Computer ScienceUS$ 148.64
US$ 33.40 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: New. New. book.
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Condition: new. Questo è un articolo print on demand.
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Deep Neural Networks in a Mathematical Framework
Book 256 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
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 -This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but alsoto those outside of the neutral network community. 84 pp. Englisch.
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Condition: New. PRINT ON DEMAND pp. 100.
