Generative Adversarial Networks in Practice
Autor Mehdi Ghayoumien Limba Engleză Hardback – 20 dec 2023
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
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Specificații
ISBN-13: 9781032248448
ISBN-10: 1032248440
Pagini: 670
Ilustrații: 10 Tables, black and white; 121 Line drawings, color; 66 Line drawings, black and white; 28 Halftones, color; 1 Halftones, black and white; 149 Illustrations, color; 67 Illustrations, black and white
Dimensiuni: 178 x 254 x 43 mm
Greutate: 1.37 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
ISBN-10: 1032248440
Pagini: 670
Ilustrații: 10 Tables, black and white; 121 Line drawings, color; 66 Line drawings, black and white; 28 Halftones, color; 1 Halftones, black and white; 149 Illustrations, color; 67 Illustrations, black and white
Dimensiuni: 178 x 254 x 43 mm
Greutate: 1.37 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
Public țintă
Adult education, Further/Vocational Education, General, and Professional ReferenceCuprins
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
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
Notă biografică
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.
Descriere
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.