Deep learning for physics research:
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo
World Scientific
[2021]
|
| Schlagwörter: | |
| Abstract: | "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"-- |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | xi, 327 Seiten Illustrationen, Diagramme |
| ISBN: | 9789811237454 |
Internformat
MARC
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| 100 | 1 | |a Erdmann, Martin |d 1960- |e Verfasser |0 (DE-588)144031760 |4 aut | |
| 245 | 1 | 0 | |a Deep learning for physics research |c Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany |
| 264 | 1 | |a New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo |b World Scientific |c [2021] | |
| 264 | 4 | |c © 2021 | |
| 300 | |a xi, 327 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 520 | 3 | |a "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"-- | |
| 650 | 0 | 7 | |a Deep Learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Physik |0 (DE-588)4045956-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenauswertung |0 (DE-588)4131193-0 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Physics / Data processing | |
| 653 | 0 | |a Physics / Research | |
| 653 | 0 | |a Machine learning | |
| 689 | 0 | 0 | |a Physik |0 (DE-588)4045956-1 |D s |
| 689 | 0 | 1 | |a Deep Learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | 2 | |a Datenauswertung |0 (DE-588)4131193-0 |D s |
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| 700 | 1 | |a Glombitza, Jonas |e Verfasser |0 (DE-588)1239183119 |4 aut | |
| 700 | 1 | |a Kasieczka, Gregor |e Verfasser |0 (DE-588)1028086423 |4 aut | |
| 700 | 1 | |a Klemradt, Uwe |d 1962- |e Verfasser |0 (DE-588)172739292 |4 aut | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032777581 | |
Datensatz im Suchindex
| _version_ | 1823932148128677888 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Erdmann, Martin 1960- Glombitza, Jonas Kasieczka, Gregor Klemradt, Uwe 1962- |
| author_GND | (DE-588)144031760 (DE-588)1239183119 (DE-588)1028086423 (DE-588)172739292 |
| author_facet | Erdmann, Martin 1960- Glombitza, Jonas Kasieczka, Gregor Klemradt, Uwe 1962- |
| author_role | aut aut aut aut |
| author_sort | Erdmann, Martin 1960- |
| author_variant | m e me j g jg g k gk u k uk |
| building | Verbundindex |
| bvnumber | BV047375862 |
| callnumber-first | Q - Science |
| callnumber-label | QC52 |
| callnumber-raw | QC52 |
| callnumber-search | QC52 |
| callnumber-sort | QC 252 |
| callnumber-subject | QC - Physics |
| classification_rvk | ST 300 ST 301 SK 955 UB 4049 |
| ctrlnum | (OCoLC)1263664392 (DE-599)KXP1761069276 |
| dewey-full | 530.0285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 530 - Physics |
| dewey-raw | 530.0285 |
| dewey-search | 530.0285 |
| dewey-sort | 3530.0285 |
| dewey-tens | 530 - Physics |
| discipline | Physik Informatik Mathematik |
| format | Book |
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| id | DE-604.BV047375862 |
| illustrated | Illustrated |
| indexdate | 2025-02-13T09:00:44Z |
| institution | BVB |
| isbn | 9789811237454 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032777581 |
| oclc_num | 1263664392 |
| open_access_boolean | |
| owner | DE-29T DE-11 DE-19 DE-BY-UBM DE-83 DE-703 DE-20 DE-188 DE-862 DE-BY-FWS |
| owner_facet | DE-29T DE-11 DE-19 DE-BY-UBM DE-83 DE-703 DE-20 DE-188 DE-862 DE-BY-FWS |
| physical | xi, 327 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | World Scientific |
| record_format | marc |
| spelling | Erdmann, Martin 1960- Verfasser (DE-588)144031760 aut Deep learning for physics research Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai ; Tokyo World Scientific [2021] © 2021 xi, 327 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index "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"-- Deep Learning (DE-588)1135597375 gnd rswk-swf Physik (DE-588)4045956-1 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf Physics / Data processing Physics / Research Machine learning Physik (DE-588)4045956-1 s Deep Learning (DE-588)1135597375 s Datenauswertung (DE-588)4131193-0 s DE-604 Glombitza, Jonas Verfasser (DE-588)1239183119 aut Kasieczka, Gregor Verfasser (DE-588)1028086423 aut Klemradt, Uwe 1962- Verfasser (DE-588)172739292 aut Erscheint auch als Online-Ausgabe 978-981-123-746-1 Erscheint auch als Online-Ausgabe, MOBI 978-981-123-747-8 |
| spellingShingle | Erdmann, Martin 1960- Glombitza, Jonas Kasieczka, Gregor Klemradt, Uwe 1962- Deep learning for physics research Deep Learning (DE-588)1135597375 gnd Physik (DE-588)4045956-1 gnd Datenauswertung (DE-588)4131193-0 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4045956-1 (DE-588)4131193-0 |
| title | Deep learning for physics research |
| title_auth | Deep learning for physics research |
| title_exact_search | Deep learning for physics research |
| title_full | Deep learning for physics research Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany |
| title_fullStr | Deep learning for physics research Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany |
| title_full_unstemmed | Deep learning for physics research Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany |
| title_short | Deep learning for physics research |
| title_sort | deep learning for physics research |
| topic | Deep Learning (DE-588)1135597375 gnd Physik (DE-588)4045956-1 gnd Datenauswertung (DE-588)4131193-0 gnd |
| topic_facet | Deep Learning Physik Datenauswertung |
| work_keys_str_mv | AT erdmannmartin deeplearningforphysicsresearch AT glombitzajonas deeplearningforphysicsresearch AT kasieczkagregor deeplearningforphysicsresearch AT klemradtuwe deeplearningforphysicsresearch |