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Tutorium "Hyperparameter Optimization"

Debus, Charlotte and Rüttgers, Alexander (2019) Tutorium "Hyperparameter Optimization". Wissensaustausch Workshop Maschinelles Lernen WAW-ML5, 03. - 05. Dez. 2019, Oberpfaffenhofen, Deutschland.

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Abstract

Part I: Theory - Train / development / test sets - Regularization techniques (dropout, L1/L2-regularization) - Optimization algorithms - Batch normalization - Grid search vs. random search vs. Bayesian optimization vs. gradient-based optimization vs. evolutionary optimization Part II: Hands-on "Hyperparameter Optimization for Improving Neural Networks " - Optimization of Jupyter notebooks using the Talos library for Keras - Good practice guidelines for hyperparameter tuning.

Item URL in elib:https://elib.dlr.de/132578/
Document Type:Conference or Workshop Item (Lecture)
Title:Tutorium "Hyperparameter Optimization"
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Debus, CharlotteCharlotte.Debus (at) dlr.dehttps://orcid.org/0000-0002-7156-2022
Rüttgers, AlexanderAlexander.Ruettgers (at) dlr.dehttps://orcid.org/0000-0001-6347-9272
Date:December 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Tutorium, Machine Learning
Event Title:Wissensaustausch Workshop Maschinelles Lernen WAW-ML5
Event Location:Oberpfaffenhofen, Deutschland
Event Type:Workshop
Event Dates:03. - 05. Dez. 2019
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
Institut of Simulation and Software Technology
Deposited By: Debus, Charlotte
Deposited On:11 Dec 2019 10:18
Last Modified:11 Dec 2019 10:18

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