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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Synthetic Data for Deep Learning (Hardcover, 1st ed. 2021)
Loot Price: R4,538
Discovery Miles 45 380
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Synthetic Data for Deep Learning (Hardcover, 1st ed. 2021)
Series: Springer Optimization and Its Applications, 174
Expected to ship within 10 - 15 working days
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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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