Mathematical aspects of deep learning (Record no. 814)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02773nam a2200217Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250408175936.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230421s2023||||xx |||||||||||||| ||eng|| |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781316516782 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 572.63 G76 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Grohs, Philipp |
| Relator term | Editor |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Gitta, Kutyniok |
| Relator term | Co-Editor |
| 245 #0 - TITLE STATEMENT | |
| Title | Mathematical aspects of deep learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | United Kingdom: |
| Name of publisher | Cambridge University Press, |
| Year of publication | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xviii, 473p.; 22cms. |
| 500 ## - GENERAL NOTE | |
| General note | In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.<br/><br/>Written by a group of leading experts in the field<br/>Presents deep learning methods from a mathematical, rather than a computer science, perspective<br/>Covers topics including generalization in deep learning, expressivity of deep neural networks, sparsity enforcing algorithms, and the scattering transform<br/>1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen<br/>2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio<br/>3. Expressivity of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok<br/>4. Optimization landscape of neural networks René Vidal, Zhihui Zhu and Benjamin D. Haeffele<br/>5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon and Klaus-Robert Müller<br/>6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montúfar<br/>7. Deep learning as sparsity enforcing algorithms A. Aberdam and J. Sulam<br/>8. The scattering transform Joan Bruna<br/>9. Deep generative models and inverse problems Alexandros G. Dimakis<br/>10. A dynamical systems and optimal control approach to deep learning Weinan E, Jiequn Han and Qianxiao Li<br/>11. Bridging many-body quantum physics and deep learning via tensor networks Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Learning Theory |
| General subdivision | Deep generative models |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Biochemistry |
| General subdivision | Scattering transform |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Neural Networks |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | P69.99 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Full call number | Accession Number | Copy number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| Dr. S. R. Ranganathan Library | Dr. S. R. Ranganathan Library | General Stacks | 572.63 G76 | 2803 | 1 | Books |