000			03422nam a22002177a 4500
005			20251012160603.0
008			251012b |||||||| |||| 00| 0 eng d
020			_a9789819780181
082			_a006.31 _bSIN
100			_aSingh, Pradeep _925552
245			_aDeep learning through the prism of tensors
260			_aCham _bSpringer _c2024
300			_axxv, 610 p.
365			_aINR _b6821.07
500			_aTable of contents: Front Matter Pages i-xxv Download chapter PDF A Tensorial Perspective to Deep Learning Pradeep Singh, Balasubramanian Raman Pages 1-27 The Algebra and Geometry of Deep Learning Pradeep Singh, Balasubramanian Raman Pages 29-70 Building Blocks Pradeep Singh, Balasubramanian Raman Pages 71-132 Journey into Convolutions Pradeep Singh, Balasubramanian Raman Pages 133-231 Modeling Temporal Data Pradeep Singh, Balasubramanian Raman Pages 233-301 Transformer Architectures Pradeep Singh, Balasubramanian Raman Pages 303-367 Attention Mechanisms Beyond Transformers Pradeep Singh, Balasubramanian Raman Pages 369-421 Graph Neural Networks: Extending Deep Learning to Graphs Pradeep Singh, Balasubramanian Raman Pages 423-482 Self-supervised and Unsupervised Learning in Deep Learning Pradeep Singh, Balasubramanian Raman Pages 483-540 Learning Representations via Autoencoders and Generative Models Pradeep Singh, Balasubramanian Raman Pages 541-585 Recent Advances and Future Perspectives Pradeep Singh, Balasubramanian Raman Pages 587-605 [https://link.springer.com/book/10.1007/978-981-97-8019-8]
520			_aIn 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. (https://link.springer.com/book/10.1007/978-981-97-8019-8)
650			_aDeep learning _915633
650			_aArtificial intelligence
700			_aRaman, Balasubramanian _925553
942			_cBK _2ddc
999			_c10480 _d10480