04400nam a22005535i 4500 978-981-97-8019-8 DE-He213 20251030114918.0 cr nn 008mamaa 250103s2024 si | s |||| 0|eng d 9789819780198 978-981-97-8019-8 10.1007/978-981-97-8019-8 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Singh, Pradeep. author. (orcid)0000-0002-5372-3355 https://orcid.org/0000-0002-5372-3355 aut http://id.loc.gov/vocabulary/relators/aut 90106 Deep Learning Through the Prism of Tensors [electronic resource] / by Pradeep Singh, Balasubramanian Raman. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXV, 610 p. 35 illus., 31 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 162 Chapter 1: A Tensorial Perspective to Deep Learning -- Chapter 2: The Algebra and Geometry of Deep Learning -- Chapter 3: Building Blocks -- Chapter 4: Journey into Convolutions -- Chapter 5: Modeling Temporal Data -- Chapter 6: Transformer Architectures -- Chapter 7: Attention Mechanisms Beyond Transformers -- Chapter 8: Graph Neural Networks: Extending Deep Learning to Graphs -- Chapter 9: Self-Supervised and Unsupervised Learning in Deep Learning -- Chapter 10: Learning Representations via Autoencoders and Generative Models -- Chapter 11: Recent Advances and Future Perspectives. In the rapidly evolving field of artificial intelligence, this book serves as a crucial resource for understanding the mathematical foundations of AI. It explores the intricate world of tensors, the fundamental elements powering today's advanced deep learning models. Combining theoretical depth with practical insights, the text navigates the complex landscape of tensor calculus, guiding readers to master the principles and applications of tensors in AI. From the basics of tensor algebra and geometry to the sophisticated architectures of neural networks, including multi-layer perceptrons, convolutional, recurrent, and transformer models, this book provides a comprehensive examination of the mechanisms driving modern AI innovations. It delves into the specifics of autoencoders, generative models, and geometric interpretations, offering a fresh perspective on the complex, high-dimensional spaces traversed by deep learning technologies. Concluding with a forward-looking view, the book addresses the latest advancements and speculates on the future directions of AI research, preparing readers to contribute to or navigate the next wave of innovations in the field. Designed for academics, researchers, and industry professionals, it serves as both an essential textbook for graduate and postgraduate students and a valuable reference for experts in the field. With its rigorous approach to the mathematical frameworks of AI and a strong focus on practical applications, this book bridges the gap between theoretical research and real-world implementation, making it an indispensable guide in the realm of artificial intelligence. Computational intelligence. 64052 Artificial intelligence. 58441 Mathematics. 65523 Computational Intelligence. 64052 Artificial Intelligence. 58441 Applications of Mathematics. 65529 Raman, Balasubramanian. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819780181 Printed edition: 9789819780204 Printed edition: 9789819780211 Studies in Big Data, 2197-6511 ; 162 68724 https://doi.org/10.1007/978-981-97-8019-8 ZDB-2-INR ZDB-2-SXIT 66318 66318