Generative Adversarial Networks in Practice

ISBN-13: 9781032248448 / Twarda / 2023 / 552 str.

Mehdi (University of San Diego) Ghayoumi

Opis

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.

Spis treści

Preface

About the Author

1. Introduction
2. Data Preprocessing
3. Model Evaluation
4. TensorFlow and Keras Fundamentals
5. Artificial Neural Networks
6. Deep Neural Networks (DNNs)
7. Generative Adversarial Networks (GANs)
8. Deep Convolutional Generative Adversarial Network (DCGAN)
9. Conditional Generative Adversarial Network (CGAN)
10. Cycle Generative Adversarial Network (CycleGAN)
11. Semi-Supervised Generative Adversarial Network (SGAN)
12. Least Squares Generative Adversarial Network (LSGAN)
13. Wasserstein Generative Adversarial Network (WGAN)
14. Generative Adversarial Networks (GANs) for Images and Video
15. Generative Adversarial Networks (GANs) for Voice, Music, and Song

　

Appendix

References

Index

O autorze

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).