A Primer on Generative Adversarial Networks

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.

A self-contained short and focused guide for practitioners and students beginning in GANs Can be used by researchers for building new and attractive tools for several applications Code-based presentation includes ready-to-run scripts that can be used for further research

Autorentext
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.

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