A Primer on Generative Adversarial Networks - Couverture souple
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Synopsis
This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.
The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.
By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Sanaa Kaddoura is Assistant Professor of Computer Science, at Zayed University, United Arab Emirates. She is also an assistant professor of business analytics for master's degree students in the UAE. Dr. Kaddoura holds a Ph.D. in computer science from Beirut Arab University, Lebanon. Dr. Kaddoura is the award winning of "Woman Leader in ICT Excellence Award" in the "22nd Middle East Women Leaders Excellence Award". She is also the award winning of the "Young Woman Researcher in Computer Science" in the 8th Venus International Women Awards (VIWA 2023). She is a fellow of Higher Education Academy, Advance HE (FHEA) since 2019, which demonstrates a personal and institutional commitment to professionalism in learning and teaching in higher education. Furthermore, she is a certified associate from Blackboard academy since April 2021. In addition to her research interests in cybersecurity, social networks, machine learning, and natural language processing, she is an active researcherin higher education teaching and learning related to enhancing the quality of instructional delivery to facilitate students' acquirement of skills and smooth transition to the workplace.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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A Primer on Generative Adversarial Networks (SpringerBriefs in Computer Science)
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Springer, 2023
ISBN 10 : 3031326601
ISBN 13 : 9783031326608
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A Primer on Generative Adversarial Networks
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners.A Primer on Generative Adversarial Networksis suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. 96 pp. Englisch. N° de réf. du vendeur 9783031326608
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A Primer on Generative Adversarial Networks
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A Primer on Generative Adversarial Networks
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ISBN 13 : 9783031326608
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A Primer on Generative Adversarial Networks
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Springer, Springer Jul 2023, 2023
ISBN 10 : 3031326601
ISBN 13 : 9783031326608
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners. A Primer on Generative Adversarial Networks is suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 96 pp. Englisch. N° de réf. du vendeur 9783031326608
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A Primer on Generative Adversarial Networks
Edité par
Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10 : 3031326601
ISBN 13 : 9783031326608
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is meant for readers who want to understand GANs without the need for a strong mathematical background. Moreover, it covers the practical applications of GANs, making it an excellent resource for beginners.A Primer on Generative Adversarial Networksis suitable for researchers, developers, students, and anyone who wishes to learn about GANs. It is assumed that the reader has a basic understanding of machine learning and neural networks. The book comes with ready-to-run scripts that readers can use for further research. Python is used as the primary programming language, so readers should be familiar with its basics.The book starts by providing an overview of GAN architecture, explaining the concept of generative models. It then introduces the most straightforward GAN architecture, which explains how GANs work and covers the concepts of generator and discriminator. The book then goes into the more advanced real-world applications of GANs, such as human face generation, deep fake, CycleGANs, and more.By the end of the book, readers will have an essential understanding of GANs and be able to write their own GAN code. They can apply this knowledge to their projects, regardless of whether they are beginners or experienced machine learning practitioners. N° de réf. du vendeur 9783031326608
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