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Analysis of Images, Social Networks and Texts : 5th International Conference, Aist 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
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Analysis of Images, Social Networks and Texts: 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected ... in Computer and Information Science, 661)
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Analysis of Images, Social Networks and Texts : 5th International Conference, Aist 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
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Analysis of Images, Social Networks and Texts : 5th International Conference, Aist 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
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Provably secure constructions in cryptography: Weak.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3843304041 ISBN 13: 9783843304047
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Analysis of Images, Social Networks and Texts : 5th International Conference, Aist 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Analysis of Images, Social Networks and Texts: 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected . in Computer and Information Science)
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Provably Secure Constructions In Cryptography: Weak Definitions Of Security And Cryptographic Constructions That Can Be Proven Secure Under These Definitions
Lingua: Inglese
Editore: Lap Lambert Academic Publishing, 2011
ISBN 10: 3843304041 ISBN 13: 9783843304047
Da: Revaluation Books, Exeter, Regno Unito
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Provably secure constructions in cryptography | Weak definitions of security and cryptographic constructions that can be proven secure under these definitions
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3843304041 ISBN 13: 9783843304047
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Provably secure constructions in cryptography | Weak definitions of security and cryptographic constructions that can be proven secure under these definitions | Sergey Nikolenko | Taschenbuch | 112 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783843304047 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Analysis of Images, Social Networks and Texts : 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
Lingua: Inglese
Editore: Springer International Publishing, 2017
ISBN 10: 3319529196 ISBN 13: 9783319529196
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016, held in Yekaterinburg, Russia, in April 2016.The 23 full papers, 7 short papers, and 3 industrial papers were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on machine learning and data analysis;social networks;natural language processing; analysis of images and video.
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Analysis of Images, Social Networks and Texts: 5th International Conference, Aist 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2017
ISBN 10: 3319529196 ISBN 13: 9783319529196
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. revised edition. 396 pages. 9.25x6.10x0.90 inches. In Stock.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Analysis of Images, Social Networks and Texts | 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers | Dmitry I. Ignatov (u. a.) | Taschenbuch | Communications in Computer and Information Science | xxiv | Englisch | 2017 | Springer | EAN 9783319529196 | 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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Synthetic Data for Deep Learning (Springer Optimization and Its Applications)
Libro 166 di 176: Springer Optimization and Its ApplicationsDa: Ria Christie Collections, Uxbridge, Regno Unito
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Synthetic Data for Deep Learning (Springer Optimization and Its Applications, 174)
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Synthetic Data for Deep Learning (Springer Optimization and Its Applications, 174)
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Synthetic Data for Deep Learning (Springer Optimization and Its Applications, 174)
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Synthetic Data for Deep Learning
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Analysis of Images, Social Networks and Texts: 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected . in Computer and Information Science, 661)
Da: Mispah books, Redhill, SURRE, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Synthetic Data for Deep Learning
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Synthetic Data for Deep Learning (Springer Optimization and Its Applications, 174)
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Synthetic Data for Deep Learning
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Synthetic Data for Deep Learning
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Synthetic Data for Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsLingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. 2021 ed. This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
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Synthetic Data for Deep Learning (Springer Optimization and Its Applications)
Libro 166 di 176: Springer Optimization and Its ApplicationsDa: Books Puddle, New York, NY, U.S.A.
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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Synthetic Data For Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsDa: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Synthetic Data for Deep Learning | Sergey I. Nikolenko | Taschenbuch | Springer Optimization and Its Applications | xii | Englisch | 2022 | Springer | EAN 9783030751807 | 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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Synthetic Data for Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsLingua: Inglese
Editore: Springer International Publishing, 2022
ISBN 10: 3030751805 ISBN 13: 9783030751807
EUR 160,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field.In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
-
Synthetic Data for Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsLingua: Inglese
Editore: Springer International Publishing, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
EUR 160,49
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field.In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
-
Synthetic Data for Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsLingua: Inglese
Editore: Springer-Nature New York Inc, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
Da: Revaluation Books, Exeter, Regno Unito
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EUR 235,89
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Aggiungi al carrelloHardcover. Condizione: Brand New. 360 pages. 9.25x6.10x1.02 inches. In Stock.
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Synthetic Data For Deep Learning
Libro 166 di 176: Springer Optimization and Its ApplicationsDa: Kennys Bookstore, Olney, MD, U.S.A.
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