Generative Adversarial Networks and Deep Learning

Regular price €67.99
Adversarial Examples
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Artificial Intelligence
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B01=Pranav D Pathak
B01=Roshani Raut
B01=Sachin R Sakhare
B01=Sonali Patil
Category1=Non-Fiction
Category=UYA
Category=UYQ
Category=UYQM
Category=UYQN
Computer Vision
Convolutional Layers
COP=United Kingdom
Deep Learning
Deep Learning Models
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Dense
Discriminator Model
Discriminator Network
DNNs
EEG Signal
eq_computing
eq_isMigrated=2
eq_new_release
eq_non-fiction
Fake Images
Id Model
Language_English
Loss Function
Machine Learning
Minority Class Samples
Nash Equilibrium
NLG
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Price_€50 to €100
Procedural Content Generation
PS=Forthcoming
Real Data Distribution
RGB
RNN
Smite
softlaunch
Spade
SVM
Underwater Image
Vice Versa

Product details

  • ISBN 9781032068114
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 19 Dec 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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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