Generative adversarial networks and deep learning : theory and applications / edited by Roshani Raut, Pranav D. Pathak, Sachin R. Sakhare, Sonali Patil

Mitwirkende(r):

Raut, Roshani, 1981- [HerausgeberIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Boca Raton : Chapman & Hall/CRC Press, 2023Auflage: First editionBeschreibung: 1 Online-Ressource (1 online resource)ISBN:

9781000840551
9781003203964

Schlagwörter:

Andere physische Formen: 1003203965 | 9781032068107. | 9781032068114. | Erscheint auch als: Generative adversarial networks and deep learning. Druck-Ausgabe. First edition. Boca Raton : CRC Press, 2023. xiv, 208 SeitenDDC-Klassifikation:

006.3/1

LOC-Klassifikation:

Q325.5

Online-Ressourcen:

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Zusammenfassung: "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"--PPN: PPN: 1882633903Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB

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