Generative Adversarial Networks and Deep Learning Theory and Applications
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 102.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
19.12.2024
Abbildungen
106 SW-Abb., 44 SW-Fotos, 62 SW-Zeichn., 12 Tabellen
Herausgeber
Roshani Raut + weitereVerlag
Taylor & FrancisSeitenzahl
224
Maße (L/B/H)
25.4/17.8/1.2 cm
Gewicht
431 g
Sprache
Englisch
ISBN
978-1-03-206811-4
This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.
A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.
Features:
- Presents a comprehensive guide on how to use GAN for images and videos.
- Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN
- Highlights the inclusion of gaming effects using deep learning methods
- Examines the significant technological advancements in GAN and its real-world application.
- Discusses as GAN challenges and optimal solutions
The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.
The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum
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