Deep Learning Explained Theory Applications And Future Directions
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DEEP LEARNING EXPLAINED: THEORY, APPLICATIONS, AND FUTURE DIRECTIONS
Author: ASHISH KUMAR , ABHISHEK DAS, SHYAMAKRISHNA SIDDHARTH CHAMARTHY, PROF. (DR) PUNIT GOEL
language: en
Publisher: DeepMisti Publication
Release Date: 2024-10-19
In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Deep Learning Explained: Theory, Applications, and Future Directions, is conceived to bridge the gap between emerging technological advancements in artificial intelligence and their strategic application across various industries. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of deep learning technologies, from foundational theories to advanced applications. We delve into the critical aspects that drive successful AI innovations in fields such as healthcare, finance, e-commerce, and autonomous systems. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, researchers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and AI adoption to the strategic management of deep learning innovations. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that deep learning and AI technologies play in shaping the future of industries and businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how deep learning can be harnessed to drive future innovations. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will shape the future of technology. Thank you for joining us on this journey. Authors
Computational Intelligence: Theories, Applications and Future Directions - Volume I
This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike.
Computational Intelligence: Theories, Applications and Future Directions - Volume II
This book presents selected proceedings of ICCI-2017, discussing theories, applications and future directions in the field of computational intelligence (CI). ICCI-2017 brought together international researchers presenting innovative work on self-adaptive systems and methods. This volume covers the current state of the field and explores new, open research directions. The book serves as a guide for readers working to develop and validate real-time problems and related applications using computational intelligence. It focuses on systems that deal with raw data intelligently, generate qualitative information that improves decision-making, and behave as smart systems, making it a valuable resource for researchers and professionals alike.