Inhaltsangabe
Generative Adversarial Networks (GANs) are transforming the field of artificial intelligence by enabling the creation of highly realistic images, pushing the boundaries of creativity and automation. These models have vast applications, from art and design to medical imaging and data augmentation, offering new possibilities across industries. Understanding GANs is essential for harnessing their potential while addressing challenges like ethical considerations and model biases. As AI-generated content becomes more prevalent, mastering these technologies will be crucial for researchers, developers, and creatives shaping the future of digital innovation. Crafting Images With Generative Adversarial Networks (GANs) and Models demystifies the complexities of GANs and provides a solid foundation for understanding and leveraging these powerful generative models. It also explores real-world applications of GANs across diverse domains, including art generation, image editing, and content creation. Covering topics such as photorealism, text-to-image, and attention mechanisms, this book is an excellent resource for data scientists, computer vision researchers, AI engineers, graphic designers, media professionals, industry practitioners, professionals, researchers, scholars, academicians, and more.
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Über die Autorin bzw. den Autor
Sapna Singh Kshatri received the Ph.D. degree from MATS University, Raipur. She has completed her M.C.A. from Rungta College of Eng. & Technology Bhilai, India. She was Assistant Professor in Ashoka Institute technology and management. She is Head of Department Of computer science In Bharti University Durg. Recently She is working as Assistant Professor in Shri Shankaracharya Institute of Professional Management and Technology (SSIPMT), In Artificial Intelligence and Machine Learning Department. She has 6 years of teaching and academic experience over 15 research article, chapters, and national and international conference and papers, published international book in IGI global, wiley, Bentham and IIP. She is received Best Paper Awards in International Conferences. She has delivered more than 5 Keynote/Invited Talks and Chaired many Technical Sessions in International/ national Conferences. She has reviewed many papers in reported journals like IEEE, IET, Oxford. Her research interests include Bio-medical, Machine Learning, and Data Science.
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Hardcover. Zustand: new. Hardcover. Generative Adversarial Networks (GANs) are transforming the field of artificial intelligence by enabling the creation of highly realistic images, pushing the boundaries of creativity and automation. These models have vast applications, from art and design to medical imaging and data augmentation, offering new possibilities across industries. Understanding GANs is essential for harnessing their potential while addressing challenges like ethical considerations and model biases. As AI-generated content becomes more prevalent, mastering these technologies will be crucial for researchers, developers, and creatives shaping the future of digital innovation. Crafting Images With Generative Adversarial Networks (GANs) and Models demystifies the complexities of GANs and provides a solid foundation for understanding and leveraging these powerful generative models. It also explores real-world applications of GANs across diverse domains, including art generation, image editing, and content creation. Covering topics such as photorealism, text-to-image, and attention mechanisms, this book is an excellent resource for data scientists, computer vision researchers, AI engineers, graphic designers, media professionals, industry practitioners, professionals, researchers, scholars, academicians, and more. "This book provides readers with a comprehensive understanding of Generative Adversarial Networks (GANs) and empower them to effectively utilize GANs for image generation purposes, from basic principles to advanced techniques"-- This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798369371527
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