Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Elektronisch E-Book |
| Sprache: | German |
| Veröffentlicht: |
Erlangen ; Nürnberg
Friedrich-Alexander-Universität Erlangen-Nürnberg
2024
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| Schriftenreihe: | Proceedings of the Institute for Multiscale Simulation
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| Schlagworte: | |
| Online-Zugang: | kostenfrei kostenfrei kostenfrei kostenfrei |
| Beschreibung: | 1 Online-Ressource |
| DOI: | 10.25593/open-fau-741 |
Internformat
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Datensatz im Suchindex
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| series2 | Proceedings of the Institute for Multiscale Simulation |
| spelling | Hall, Clemens Verfasser (DE-588)134234989X aut Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn von Clemens Hall Erlangen ; Nürnberg Friedrich-Alexander-Universität Erlangen-Nürnberg 2024 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Proceedings of the Institute for Multiscale Simulation Masterarbeit Friedrich-Alexander-Universität Erlangen-Nürnberg 2018 Archivierung/Langzeitarchivierung gewährleistet DE-101 pdager 4\p Poröser Stoff (DE-588)4046811-2 gnd 5\p Maschinelles Lernen (DE-588)4193754-5 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd 7\p TensorFlow (DE-588)1153577011 gnd pourous media, characterization, machine learning, CT tomograms (DE-588)4113937-9 Hochschulschrift gnd-content https://open.fau.de/handle/openfau/31254 Verlag kostenfrei Volltext https://doi.org/10.25593/open-fau-741 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:101:1-2406070506447.547535709874 Resolving-System kostenfrei Volltext https://d-nb.info/1331965969/34 Langzeitarchivierung Nationalbibliothek kostenfrei Volltext 1\p emakn 0,18771 20240608 DE-101 https://d-nb.info/provenance/plan#emakn 2\p emasg 0,38851 20240608 DE-101 https://d-nb.info/provenance/plan#emasg 3\p npi 20240607 DE-101 https://d-nb.info/provenance/plan#npi 4\p emagnd 0,34966 20240608 DE-101 https://d-nb.info/provenance/plan#emagnd 5\p emagnd 0,27093 20240608 DE-101 https://d-nb.info/provenance/plan#emagnd 6\p emagnd 0,09897 20240608 DE-101 https://d-nb.info/provenance/plan#emagnd 7\p emagnd 0,05547 20240608 DE-101 https://d-nb.info/provenance/plan#emagnd |
| spellingShingle | Hall, Clemens Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn 4\p Poröser Stoff (DE-588)4046811-2 gnd 5\p Maschinelles Lernen (DE-588)4193754-5 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd 7\p TensorFlow (DE-588)1153577011 gnd |
| subject_GND | (DE-588)4046811-2 (DE-588)4193754-5 (DE-588)4226127-2 (DE-588)1153577011 (DE-588)4113937-9 |
| title | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn |
| title_auth | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn |
| title_exact_search | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn |
| title_full | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn von Clemens Hall |
| title_fullStr | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn von Clemens Hall |
| title_full_unstemmed | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn von Clemens Hall |
| title_short | Charakterisierung poröser Medien mittels Machine Learning in Python mit scikit−learn, Tensorflow und tflearn |
| title_sort | charakterisierung poroser medien mittels machine learning in python mit scikit−learn tensorflow und tflearn |
| topic | 4\p Poröser Stoff (DE-588)4046811-2 gnd 5\p Maschinelles Lernen (DE-588)4193754-5 gnd 6\p Neuronales Netz (DE-588)4226127-2 gnd 7\p TensorFlow (DE-588)1153577011 gnd |
| topic_facet | Poröser Stoff Maschinelles Lernen Neuronales Netz TensorFlow Hochschulschrift |
| url | https://open.fau.de/handle/openfau/31254 https://doi.org/10.25593/open-fau-741 https://nbn-resolving.org/urn:nbn:de:101:1-2406070506447.547535709874 https://d-nb.info/1331965969/34 |
| work_keys_str_mv | AT hallclemens charakterisierungporosermedienmittelsmachinelearninginpythonmitscikitlearntensorflowundtflearn |