Condizione: New. pp. 100 1st ed. 2018 edition NO-PA16APR2015-KAP.
Da: Majestic Books, Hounslow, Regno Unito
EUR 74,07
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New. pp. 100.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 71,09
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: Chiron Media, Wallingford, Regno Unito
EUR 68,93
Quantità: 10 disponibili
Aggiungi al carrelloPF. Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Da: moluna, Greven, Germania
EUR 66,44
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Da: Revaluation Books, Exeter, Regno Unito
EUR 109,44
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 84 pages. 9.00x6.00x0.25 inches. In Stock.
Da: preigu, Osnabrück, Germania
EUR 68,25
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer, Berlin, Springer, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 79,16
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 128,85
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. New. book.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 62,23
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10: 3319753037 ISBN 13: 9783319753034
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 74,89
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 107,60
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 100.