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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3843304041 ISBN 13: 9783843304047
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Idioma: Inglés
Publicado por Lap Lambert Academic Publishing, 2011
ISBN 10: 3843304041 ISBN 13: 9783843304047
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.
Idioma: Inglés
Publicado por Lap Lambert Academic Publishing, 2011
ISBN 10: 3843304041 ISBN 13: 9783843304047
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.
Idioma: Inglés
Publicado por Springer International Publishing, Springer International Publishing Feb 2017, 2017
ISBN 10: 3319529196 ISBN 13: 9783319529196
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 396 pp. Englisch.
Idioma: Inglés
Publicado por Springer International Publishing, 2017
ISBN 10: 3319529196 ISBN 13: 9783319529196
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: 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.
Idioma: Inglés
Publicado por Springer-Verlag New York Inc, 2017
ISBN 10: 3319529196 ISBN 13: 9783319529196
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. revised edition. 396 pages. 9.25x6.10x0.90 inches. In Stock.
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Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 191,81
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Añadir al carritoHardback. Condición: 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.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 202,97
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Añadir al carritoCondición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
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Idioma: Inglés
Publicado por Springer International Publishing, 2022
ISBN 10: 3030751805 ISBN 13: 9783030751807
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
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Añadir al carritoTaschenbuch. Condición: 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.
Idioma: Inglés
Publicado por Springer International Publishing, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: 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.
Idioma: Inglés
Publicado por Springer-Nature New York Inc, 2021
ISBN 10: 3030751775 ISBN 13: 9783030751777
Librería: Revaluation Books, Exeter, Reino Unido
EUR 233,13
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Añadir al carritoHardcover. Condición: Brand New. 360 pages. 9.25x6.10x1.02 inches. In Stock.