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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 | ||||||
| Gold Open Access: | No | ||||||
| In SCOPUS: | No | ||||||
| In ISI Web of Science: | No | ||||||
| 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 Technology | ||||||
| DLR - Research area: | Raumfahrt | ||||||
| DLR - Program: | R SY - Technik für Raumfahrtsysteme | ||||||
| DLR - Research theme (Project): | R - Vorhaben SISTEC | ||||||
| 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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