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Synopsis
Generative Adversarial Networks (GANs) and Meta-Learning synergies can be combined and leveraged to enhance the capabilities of artificial intelligence (AI) systems, particularly in areas such as image generation, style transfer, few-shot learning, and domain adaptation. These techniques can be integrated to develop more robust and efficient AI models. Ultimately, understanding the theoretical foundations, implementation strategies, and practical applications of GANs and Meta-Learning can be used to address complex real-world challenges. Exploring Generative Adversarial Networks and Meta-Learning Synergies explores the intersection and synergy between two cutting-edge AI techniques: GANs and Meta-Learning. It showcases the potential of these synergies in advancing the field of AI and addressing complex real-world challenges. Covering topics such as neuromorphic computing, transfer learning, and visual speech recognition, this book is an excellent resource for computer scientists, entrepreneurs, healthcare professionals, professionals, researchers, scholars, academicians, and more.
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About the Authors
Sarita Simaiya is a dedicated and accomplished professional with over 16 years of experience in research and teaching in the field of Computer Science and Engineering. Currently serving as a Professor at Galgotias University, she holds a Ph.D. and M.Tech in Computer Science and Engineering. Dr. Sarita is known for her expertise in AI and ML, and she has made significant contributions to the field through her research and publications. She is passionate about mentoring students and creating a dynamic learning environment that fosters innovation and excellence. Dr. Sarita's commitment to academic and research excellence makes her a valuable asset to Galgotias
Umesh Kumar Lilhore is currently a Professor at the School of Computing Science & Engineering (CSE) at Galgotia University, Greater Noida. With over 19 years of teaching and 8 years of research experience, he has previously held positions at various renowned universities and colleges in India and abroad. Dr. Lilhore holds a Ph.D. and M.Tech in CSE and has completed his postdoctoral research at the Institute of Advanced Computing, University of Louisiana at Lafayette. He has a strong publication record with articles in reputed, peer-reviewed national and international Scopus journals and conferences.
Yogesh Sharma is a highly experienced professional with a strong background in Computer Science and Engineering. He currently serves as a Professor in the Department of Computer Science and Engineering at KL University. Dr. Yogesh holds a Ph.D. in Computer Science and Engineering and has over 18 years of teaching and research experience. Education: Ph.D. in Computer Science and Engineering Professional Experience: Professor, Computer Science and Engineering, KL University (Present)
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Hardcover. Condition: new. Hardcover. Generative Adversarial Networks (GANs) and Meta-Learning synergies can be combined and leveraged to enhance the capabilities of artificial intelligence (AI) systems, particularly in areas such as image generation, style transfer, few-shot learning, and domain adaptation. These techniques can be integrated to develop more robust and efficient AI models. Ultimately, understanding the theoretical foundations, implementation strategies, and practical applications of GANs and Meta-Learning can be used to address complex real-world challenges. Exploring Generative Adversarial Networks and Meta-Learning Synergies explores the intersection and synergy between two cutting-edge AI techniques: GANs and Meta-Learning. It showcases the potential of these synergies in advancing the field of AI and addressing complex real-world challenges. Covering topics such as neuromorphic computing, transfer learning, and visual speech recognition, this book is an excellent resource for computer scientists, entrepreneurs, healthcare professionals, professionals, researchers, scholars, academicians, and more. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369375754
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