Book Description: 'Generative Adversarial Networks in Practice' is a comprehensive guide to understanding and implementing generative adversarial networks (GANs) in real-world applications. The book covers the fundamental concepts of GANs, their training, and practical use cases in computer vision, natural language processing, and other domains. It provides a step-by-step approach to implementing GANs using popular deep learning frameworks such as TensorFlow and PyTorch. The book also discusses the challenges and limitations of GANs and offers solutions to overcome them. The book is divided into four parts: Part I provides an overview of GANs, including their history, key components, and basic principles. Part II delves into the implementation of GANs in computer vision tasks, such as image generation, image-to-image translation, and image denoising. Part III explores the application of GANs in natural language processing, including text generation and language modeling. Finally, Part IV discusses the challenges and limitations of GANs and provides solutions to overcome them. The book is written for researchers, practitioners, and students who want to understand and apply GANs in their work.

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