Editore: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 49,41
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Editore: LAP LAMBERT Academic Publishing Jun 2019, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 32,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -The purpose of this book chapter is to provide a approach to explore the Machine's Capability in generating human-level products as well as problem solving. Sculptures became a part of finest medium in South Indian Expression in form of dance. This book chapter entails the use of GAN (Generative Adversarial Network) in Deep Neural Net Architectures called the Generator (New Data Instances) and Discriminator (Authenticity). GAN has the following phases. The First Phase Generator takes input in random numbers and return an image. In the Second Phase the Generated Image is fed into the Discriminator. In the Final Phase the Discriminator takes both real and fake images and return probabilities for representing Authenticity. The reader is expected to be familiar with the working process and basic features of GAN.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 49,98
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Editore: LAP LAMBERT Academic Publishing Jun 2019, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 32,90
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The purpose of this book chapter is to provide a approach to explore the Machine's Capability in generating human-level products as well as problem solving. Sculptures became a part of finest medium in South Indian Expression in form of dance. This book chapter entails the use of GAN (Generative Adversarial Network) in Deep Neural Net Architectures called the Generator (New Data Instances) and Discriminator (Authenticity). GAN has the following phases. The First Phase Generator takes input in random numbers and return an image. In the Second Phase the Generated Image is fed into the Discriminator. In the Final Phase the Discriminator takes both real and fake images and return probabilities for representing Authenticity. The reader is expected to be familiar with the working process and basic features of GAN. 60 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 52,06
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Editore: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 29,02
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sheik Abdul Kareem JainulabudeenJainulabudeen Sheik Abdul Kareem received M.Tech in Computer Science and Engineering from B.S. Abdur Rahman University. He is currently working as Assistant Professor in Panimalar Engineering College, .
Editore: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200216584 ISBN 13: 9786200216588
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 34,42
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The purpose of this book chapter is to provide a approach to explore the Machine's Capability in generating human-level products as well as problem solving. Sculptures became a part of finest medium in South Indian Expression in form of dance. This book chapter entails the use of GAN (Generative Adversarial Network) in Deep Neural Net Architectures called the Generator (New Data Instances) and Discriminator (Authenticity). GAN has the following phases. The First Phase Generator takes input in random numbers and return an image. In the Second Phase the Generated Image is fed into the Discriminator. In the Final Phase the Discriminator takes both real and fake images and return probabilities for representing Authenticity. The reader is expected to be familiar with the working process and basic features of GAN.