A Primer on Generative Adversarial Networks (SpringerBriefs in Computer Science)
Sanaa Kaddoura
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.Издательство:
Springer
Язык:
english
Страницы:
94
ISBN 10:
3031326601
ISBN 13:
9783031326608
Файл:
EPUB, 26.47 MB
IPFS:
,
english0
Скачивание этой книги недоступно по жалобе правообладателя
Beware of he who would deny you access to information, for in his heart he dreams himself your master
Конвертируйте файлы
Больше результатов поиска
Другие преимущества 
