9783030751807 - synthetic data for deep learning: 174 di nikolenko, sergey i. (12 risultati)
Lingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Altre immaginiLingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Taschenbuch. 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[do…t]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer International Publishing, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Taschenbuch. 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 subfie…lds 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.
Lingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Jun 2022, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 360 pp. Englisch.
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .
Lingua: Inglese
Editore: Springer, Springer Nature Switzerland Jun 2022, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 impo…rtant 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 360 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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Condizione: New. Print on Demand.
Lingua: Inglese
Editore: Springer, 2022
Serie: Springer Optimization and Its Applications, Libro 166 di 176. Libro 166 di 176 - Springer Optimization and Its Applications
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