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A primer on generative adversarial n...

Kaddoura, Sanaa.

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A primer on generative adversarial networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A primer on generative adversarial networks/ by Sanaa Kaddoura.
作者:	Kaddoura, Sanaa.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	x, 84 p. :ill., digital ;24 cm.
內容註:	Overview of GAN Structure -- Your First GAN -- Real World Applications -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-031-32661-5
ISBN:	9783031326615

A primer on generative adversarial networks
Kaddoura, Sanaa.

A primer on generative adversarial networks[electronic resource] /by Sanaa Kaddoura. - Cham :Springer International Publishing :2023. - x, 84 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Overview of GAN Structure -- Your First GAN -- Real World Applications -- Conclusion.

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.

ISBN: 9783031326615

Standard No.: 10.1007/978-3-031-32661-5doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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