Deep learning for physics research
Contributor(s): Erdmann, Martin | Glombitza, Jonas | Kasieczka, Gregor | Klemradt, Uwe.
Publisher: Chennai World Scientific Publishing 2021Description: xi, 327p.ISBN: 9789811285325.Subject(s): Physics | Machine learningDDC classification: 530.0285 | D36 Summary: "A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded"--| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Text Books
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PK Kelkar Library, IIT Kanpur | TEXT | 530.0285 D36 cop.1 (Browse shelf) | Copy 1 | Available | A186563 | ||
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Text Books
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PK Kelkar Library, IIT Kanpur | TEXT | 530.0285 D36 cop.2 (Browse shelf) | Copy 2 | Available | A186564 | ||
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Text Books
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PK Kelkar Library, IIT Kanpur | TEXT | 530.0285 D36 cop.3 (Browse shelf) | Copy 3 | Available | A186565 | ||
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Text Books
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PK Kelkar Library, IIT Kanpur | TEXT | 530.0285 D36 cop.4 (Browse shelf) | Copy 4 | Available | A186566 |
Total holds: 0
Browsing PK Kelkar Library, IIT Kanpur Shelves , Collection code: TEXT Close shelf browser
| 530.0285 D36 cop.1 Deep learning for physics research | 530.0285 D36 cop.2 Deep learning for physics research | 530.0285 D36 cop.3 Deep learning for physics research | 530.0285 D36 cop.4 Deep learning for physics research | 530.078 K636e Experimental methods | 530.1 J595m cop.2 Mathematical physics | 530.1 J595m cop.3 Mathematical physics |
"A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded"--
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