Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.
Key features:
- Guides you through the complex world of GANs, demystifying their intricacies.
- Accompanies your learning journey with real-world examples and practical applications.
- Navigates the theory behind GANs, presenting it in an accessible and comprehensive way.
- Simplifies the implementation of GANs using popular deep learning platforms.
- Introduces various GAN architectures, giving readers a broad view of their applications.
- Nurture your knowledge of AI with our comprehensive yet accessible content.
- Practice your skills with numerous case studies and coding examples.
- Reviews advanced GANs such as DCGAN, CGAN, CycleGAN, and more, with clear explanations and practical examples.
- Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs.
- Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject.
- Takes you through GAN applications across different data types, highlighting their versatility.
- Inspires the reader to explore beyond the book, fostering an environment conducive to independent learning and research.
- Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge.
- Empowers you with the skills and knowledge needed to confidently use GANs in your projects.
Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
About the Author:
Dr. Mehdi Ghayoumi currently holds a distinguished position as an Assistant Professor at the renowned Center of Criminal Justice, Intelligence, and Cybersecurity at the State University of New York (SUNY) at Canton. His past positions bear testament to his academic excellence and leadership. He has previously served as a Research Assistant Professor at SUNY Binghamton, where he took on the dynamic role of spearheading initiatives at the Media Core Lab. Moreover, his academic journey includes a noteworthy stint as a lecturer at Kent State University, where his exceptional teaching abilities were recognized with a prestigious Teaching Award for two consecutive years, 2016 and 2017.
Over the years, Dr. Ghayoumi has not only been instrumental in teaching but has also taken the lead in developing comprehensive courses in domains as diverse and interconnected as machine learning, data science, robotics, and programming. His research interests provide a broad glimpse into his scientific pursuits. From Machine Learning to Machine Vision, and from Robotics to Human-Robot Interaction (HRI) and privacy, Dr. Ghayoumi's research spans a broad spectrum. His research, focusing on creating viable systems for realistic environment settings, demonstrates his commitment to practical applications. His current projects cut across multiple fields, including Human-Robot Interaction, manufacturing, biometrics, and healthcare.
In addition to his research and teaching commitments, Dr. Ghayoumi is actively involved in the broader academic community. He is a member of the technical program committee for numerous conferences and workshops and serves on the editorial board of an array of respected journals in the realms of machine learning, mathematics, and robotics. These include but are not limited to ICML, ICPR, HRI, FG, WACV, IROS, CIBCB, and JAI.
Dr. Ghayoumi's substantial contributions to his field are not confined to his active participation in these committees. His research has found a receptive audience at several leading conferences and in prestigious journals in related fields. Noteworthy among these are Human-Computer Interaction (HRI), Robotics Science and Systems (RSS), and the International Conference on Machine Learning and Applications (ICMLA).
