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.
Key features:
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- uides you through the complex world of GANs, demystifying their intricacies.
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- ccompanies your learning journey with real-world examples and practical applications.
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- avigates the theory behind GANs, presenting it in an accessible and comprehensive way.
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- implifies the implementation of GANs using popular deep learning platforms.
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- ntroduces various GAN architectures, giving readers a broad view of their applications.
- N
- urture your knowledge of AI with our comprehensive yet accessible content.
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- ractice your skills with numerous case studies and coding examples.
- R
- eviews advanced GANs such as DCGAN, CGAN, CycleGAN, and more, with clear explanations and practical examples.
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- dapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs.
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- onnects the dots between GAN theory and practice, providing a well-rounded understanding of the subject.
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- akes you through GAN applications across different data types, highlighting their versatility.
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- nspires the reader to explore beyond the book, fostering an environment conducive to independent learning and research.
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- loses the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge.
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- mpowers 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.
Preface
About the Author
- Introduction
- Data Preprocessing
- Model Evaluation
- TensorFlow and Keras Fundamentals
- Artificial Neural Networks
- Deep Neural Networks (DNNs)
- Generative Adversarial Networks (GANs)
- Deep Convolutional Generative Adversarial Network (DCGAN)
- Conditional Generative Adversarial Network (CGAN)
- Cycle Generative Adversarial Network (CycleGAN)
- Semi-Supervised Generative Adversarial Network (SGAN)
- Least Squares Generative Adversarial Network (LSGAN)
- Wasserstein Generative Adversarial Network (WGAN)
- Generative Adversarial Networks (GANs) for Images and Video
- Generative Adversarial Networks (GANs) for Voice, Music, and Song
Appendix
References
Index
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. Ghayoumis 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.
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