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.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
SpringerBriefs in Computer Science |
| Release date: |
July 2023 |
| First published: |
2023 |
| Authors: |
Sanaa Kaddoura
|
| Dimensions: |
235 x 155mm (L x W) |
| Pages: |
84 |
| Edition: |
1st ed. 2023 |
| ISBN-13: |
978-3-03-132660-8 |
| Categories: |
Books
|
| LSN: |
3-03-132660-1 |
| Barcode: |
9783031326608 |
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