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Author: Sergey I. Nikolenko Publisher: Springer Nature ISBN: 3030751783 Category : Computers Languages : en Pages : 348
Book Description
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.
Author: Sergey I. Nikolenko Publisher: Springer Nature ISBN: 3030751783 Category : Computers Languages : en Pages : 348
Book Description
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.
Author: Gert Jan Hofstede Publisher: Nicholas Brealey ISBN: 0585485909 Category : Social Science Languages : en Pages : 217
Book Description
A masterpiece in intercultural training! Exploring Culture brings Geert Hofstede's five dimensions of national culture to life. Gert Jan Hofstede and his co-authors Paul Pedersen and Geert Hofstede introduce synthetic cultures, the ten "pure" cultural types derived from the extremes of the five dimensions. The result is a playful book of practice that is firmly rooted in theory. Part light, part serious, but always thought-provoking, this unique book approaches training through the three-part process of building awareness, knowledge, and skills. It leads the reader through the first two components with more than 75 activities, dialogues, stories, and incidents. The Synthetic Culture Laboratory and two full simulations fulfill the skill-building component. Exploring Culture is suitable for students, trainers, coaches and educators. It can be used for individual study or as a text, and it serves as an excellent partner to Geert Hofstede's popular Cultures and Organizations.
Author: Paul C. Guest Publisher: Humana ISBN: 9781071615607 Category : Science Languages : en Pages : 0
Book Description
This detailed book explores protocols with the aim of testing aerobic exercise, resistance training, special diets, additives and natural products, which have led to new insights into the physiological and molecular aspects of health and disease. Many of these approaches have contributed to significant improvements in disease areas such as cardiovascular disease, cognitive dysfunction, diabetes, frailty, gliobastoma, metabolic syndrome, obesity, oxidative stress, and various cancers. This collection also provides important information on disease mechanisms and novel drug targets as each protocol is presented in the context of specific chronic diseases or different therapeutic areas. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and reflective of research from all around the globe, Physical Exercise and Natural and Synthetic Products in Health and Disease serves as an ideal guide for researchers in the areas of chronic disease, exercise, and nutrition, as well as to clinical scientists, physicians, and pharmacologists as it gives insights into possibilities for the development of novel therapeutics and the means of monitoring therapeutic response through the measurement of molecular and physiometric biomarkers.
Author: Susan G. Straus Publisher: ISBN: 9781977401328 Category : Business & Economics Languages : en Pages : 0
Book Description
The U.S. Army uses virtual systems for collective skills training. This report examines the needs for fidelity in simulators and associated costs to support effective and efficient collective training.
Author: Sergey I. Nikolenko
Author: Sergey I. Nikolenko
Author: Gert Jan Hofstede
Author: Human Resources Research Organization
Author: Paul C. Guest
Author: United States. Army Air Forces
Author: 
Author: Nicholas Hobbs
Author: 
Author: 
Author: Susan G. Straus