Explores the practical applications of generative models using TensorFlow, one of the most powerful machine learning libraries. This book guides readers through the essential concepts of generative AI, including neural networks, generative adversarial networks (GANs), and autoencoders. It covers how to use TensorFlow to build and train these models efficiently, with hands-on tutorials that allow users to create their own AI-driven applications. The book is structured to take readers from basic TensorFlow operations to advanced generative model techniques, offering a blend of theoretical explanations and practical exercises. Key topics include data preparation, model architecture, hyperparameter tuning, and deployment of generative AI models. It also addresses common challenges, such as managing large datasets, avoiding mode collapse in GANs, and optimizing model performance. Ideal for AI enthusiasts, data scientists, and developers, this book provides a clear roadmap for building generative AI solutions, complete with real-world examples and case studies across different domains such as art, natural language processing, and synthetic data generation. By the end of the book, readers will have the skills to create cutting-edge generative models using TensorFlow.
