Paperback. 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 GANHighlights the inclusion of gaming effects using deep learning methodsExamines the significant technological advancements in GAN and its real-world application.Discusses as GAN challenges and optimal solutionsThe 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 This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. It concentrates on cutting-edge research in DL and GAN which includes creating new tools and methods for processing text, images, and audio. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Bibliographic Details

Title: Generative Adversarial Networks and Deep Learning (Paperback)
Publisher: Taylor & Francis Ltd
Publication Date: 2024
Language: English
ISBN 10: 1032068116
ISBN 13: 9781032068114
Binding: Paperback
Condition: new

About this title

Synopsis

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

"About this title" may belong to another edition of this title.

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