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.
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.
Dr. Mehdi Ghayoumi is an Assistant Professor at the State University of New York (SUNY) at Canton. With a strong focus on cutting-edge technologies, he has dedicated his expertise to areas including Machine Learning, Machine Vision, Robotics, Human-Robot Interaction (HRI), and privacy. Dr. Ghayoumi¿s research revolves around constructing sophisticated systems tailored to address the complexities and challenges within these fields, driving innovation and advancing the forefront of knowledge in his respective areas of expertise.
Inhaltsangabe
1. Introduction 2. Data Preprocessing 3. Model Evaluation 4. TensorFlow and Keras Fundamentals 5. Artificial Neural Networks Fundamentals and Architectures 6. Deep Neural Networks (DNNs) Fundamentals and Architectures 7. Generative Adversarial Networks (GANs) Fundamentals and Architectures 8. Deep Convolutional Generative Adversarial Networks (DCGANs) 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 15. Generative Adversarial Networks (GANs) for Voice, Music, and Song Appendix
