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This book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (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, and CycleGAN, 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 this 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.
List of contents
1. Introduction2. Data Preprocessing3. Model Evaluation4. TensorFlow and Keras Fundamentals5. Artificial Neural Networks Fundamentals and Architectures6. Deep Neural Networks (DNNs) Fundamentals and Architectures7. Generative Adversarial Networks (GANs) Fundamentals and Architectures8. 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 Images15. Generative Adversarial Networks (GANs) for Voice, Music, and SongAppendix
About the author
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.
