Synthetic Data for Deep Learning

Lingua: inglese

Editore: Springer, Springer Nature Switzerland Jun 2021, 2021

3030751775 / 9783030751777

Serie: Libro 166 di 176 - Springer Optimization and Its Applications

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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Venditore AbeBooks dal 23 gennaio 2017

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Descrizione dell’articolo da parte del venditore

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 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 360 pp. Englisch.…

Codice articolo 9783030751777

Titolo
Synthetic Data for Deep Learning
Autore
Sergey I. Nikolenko
Editore
Springer, Springer Nature Switzerland Jun 2021
Anno di pubblicazione
2021
Condizione
Neu
Rilegatura
Buch
Lingua
inglese
ISBN 10
3030751775
ISBN 13
9783030751777
Peso dell'articolo
705 grammi
Dimensioni
241x160x25 mm
Serie
Libro 166 di 176: Springer Optimization and Its Applications

buchversandmimpf2000

Emtmannsberg, BAYE, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 60 a 60 giorni lavorativiDa 60 a 60 giorni lavorativi
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