MARC details
| 000 -LEADER |
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| fixed length control field |
03422nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251012160603.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251012b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789819780181 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
SIN |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Singh, Pradeep |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep learning through the prism of tensors |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc. |
Cham |
| Name of publisher, distributor, etc. |
Springer |
| Date of publication, distribution, etc. |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxv, 610 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
6821.07 |
| 500 ## - GENERAL NOTE |
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| General note |
Table of contents:<br/>Front Matter<br/>Pages i-xxv<br/>Download chapter PDF <br/>A Tensorial Perspective to Deep Learning<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 1-27<br/>The Algebra and Geometry of Deep Learning<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 29-70<br/>Building Blocks<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 71-132<br/>Journey into Convolutions<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 133-231<br/>Modeling Temporal Data<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 233-301<br/>Transformer Architectures<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 303-367<br/>Attention Mechanisms Beyond Transformers<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 369-421<br/>Graph Neural Networks: Extending Deep Learning to Graphs<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 423-482<br/>Self-supervised and Unsupervised Learning in Deep Learning<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 483-540<br/>Learning Representations via Autoencoders and Generative Models<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 541-585<br/>Recent Advances and Future Perspectives<br/>Pradeep Singh, Balasubramanian Raman<br/>Pages 587-605<br/><br/>[https://link.springer.com/book/10.1007/978-981-97-8019-8] |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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.<br/><br/>(https://link.springer.com/book/10.1007/978-981-97-8019-8) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Raman, Balasubramanian |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
