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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