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Shonia, Ilona (2018) Machbarkeitsstudie des L-BFGS Verfahrens für das Training von Deep Learning Problemen. Bachelor's, Universität zu Köln.
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Abstract
Diese Arbeit beschäftigt sich mit der Anwendbarkeit von Methoden zweiter Ordnung aus der Quasi-Newton Klasse für das Training von Deep Learning Problemen
| Item URL in elib: | https://elib.dlr.de/124414/ | ||||||||
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| Document Type: | Thesis (Bachelor's) | ||||||||
| Title: | Machbarkeitsstudie des L-BFGS Verfahrens für das Training von Deep Learning Problemen | ||||||||
| Authors: |
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| Date: | October 2018 | ||||||||
| Refereed publication: | Yes | ||||||||
| Open Access: | Yes | ||||||||
| Number of Pages: | 43 | ||||||||
| Status: | Published | ||||||||
| Keywords: | Optimierungsalgorithmen für Deep Learning | ||||||||
| Institution: | Universität zu Köln | ||||||||
| Department: | Mathematisches Institut | ||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||
| HGF - Program: | Space | ||||||||
| HGF - Program Themes: | Space System Technology | ||||||||
| DLR - Research area: | Raumfahrt | ||||||||
| DLR - Program: | R SY - Space System Technology | ||||||||
| DLR - Research theme (Project): | R - Vorhaben SISTEC (old) | ||||||||
| Location: | Köln-Porz | ||||||||
| Institutes and Institutions: | Institut of Simulation and Software Technology > High Performance Computing | ||||||||
| Deposited By: | Shonia, Ilona | ||||||||
| Deposited On: | 06 Dec 2018 14:33 | ||||||||
| Last Modified: | 31 Jul 2019 20:22 |
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