7 New Generative Adversarial Network Books Reshaping 2025

Drawing from his extensive work leading the Generative AI group at Eindhoven University of Technology, Jakub M. Tomczak offers a detailed exploration of deep generative models that goes beyond surface-level introductions. You will gain a solid grasp of major model classes such as mixture models, autoregressive models, flow-based models, and score-based generative models, supported by practical code examples in Python and PyTorch. The book suits those with foundational knowledge in calculus, linear algebra, probability, and machine learning, aiming to deepen understanding and build skills to design and implement new generative models. While it's technical, the inclusion of real-world applications like neural compression helps ground the theories in practice, making it a fitting resource for students, researchers, and engineers across various scientific fields.

Marija Jegorova, a deep learning researcher focused on Generative Adversarial Networks, offers a practical guide to mastering GANs using PyTorch 2.x. You’ll move beyond theory to build, train, and optimize diverse GAN architectures such as StyleGAN, CycleGAN, and pix2pix, gaining proficiency in generating images, videos, audio, and even 3D models. The book also explores hybrid models that combine GANs with Transformers and Diffusion Models, giving you insights into the latest advances in generative AI. If you’re comfortable with Python and want hands-on expertise in applying GANs to complex problems, this book speaks directly to your goals.

This tailored book explores the forefront of generative adversarial network (GAN) technology as it stands in 2025. It examines recent breakthroughs and innovations, providing a customized exploration that matches your background and interests in this rapidly evolving field. The content delves into emerging architectures, novel training techniques, and cutting-edge applications, focusing on the discoveries that are shaping the future of GANs. By addressing your specific goals, the book offers a focused, personalized journey through the latest research and trends, allowing you to engage deeply with the material that matters most to your learning and professional growth.

When Jonathan M. Sterling noticed many struggling to grasp the rapidly evolving field of generative AI, he set out to create a clear, approachable guide. This book demystifies complex AI models like GANs and large language models through accessible explanations and practical examples, including case studies on AI in healthcare and finance. You’ll gain a solid foundation as well as hands-on skills using tools like TensorFlow and PyTorch, along with over 350 ChatGPT prompts to inspire your own projects. If you’re eager to understand and apply generative AI without getting lost in jargon, this book offers a well-structured path to build your expertise.

Nicholas Dempsey’s approach demystifies the often intimidating field of generative AI by breaking down core concepts from neural networks to machine learning in accessible terms. You’ll learn not just how these models work, but also how to harness them creatively—generating images, music, and text with AI tools. The book also ventures into practical territory by exploring ways to monetize these skills, offering strategies for turning AI knowledge into income. This makes it especially useful if you’re starting out in AI or looking to blend creativity with technology and entrepreneurship.

What if everything you knew about building generative AI models was challenged by this concise yet insightful guide? Anand Vemula, with his extensive 27-year career spanning CXO roles and diverse industries like healthcare and energy, distills complex concepts into practical frameworks that you can apply quickly. You'll learn to set up your environment, build and train GANs, and explore advanced variations like StyleGANs and CycleGANs, all while considering ethical implications and real-world applications in sectors such as finance and marketing. If you're aiming to grasp both foundational and emerging generative AI techniques without wading through jargon, this book fits that need well.

This tailored book on generative adversarial networks (GANs) explores the forefront of 2025 developments and discoveries, offering you a uniquely focused learning experience. By matching your background and interests, it delves into the emerging research, recent breakthroughs, and evolving techniques that define next-generation GANs. The content examines advanced architectures, practical challenges, and innovative applications, equipping you with up-to-date understanding tailored specifically to your goals. This personalized approach ensures you engage deeply with the latest knowledge, avoiding generic overviews and instead focusing on what truly matters for your growth in this dynamic field.

Alan Miller addresses the rapid evolution of generative AI by breaking down its complexities into clear, approachable lessons for beginners. You’ll explore foundational concepts of generative adversarial networks and machine learning, plus practical chapters on prompt engineering and Python basics that equip you to start creating AI-driven projects. Miller’s approach suits anyone curious about AI’s creative potential—from aspiring developers to artists wanting hands-on experience with emerging tools. While it doesn’t dive deeply into advanced research, this book gives you a solid grasp of generative AI’s mechanics and real-world applications across industries like healthcare and finance.

Unlike most books on GANs that focus on image generation, this work by computer science expert Sivaram Ponnusamy and editors Jilali Antari and Pawan R Bhaladhare dives into applying Generative Adversarial Networks as a robust tool for enhancing security in public environments. You’ll learn about the adversarial training mechanisms that make GANs effective in detecting and mitigating cyber threats, along with the ethical dimensions of deploying such technology. The book carefully balances technical depth with strategic insights, making it suitable for those seeking to understand how GANs can safeguard data privacy beyond their traditional use cases. If your interest lies in cutting-edge cybersecurity methods intertwined with AI, this book will expand your toolkit without overselling its scope.