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dc.contributor.advisorHensel, Marc-
dc.contributor.authorSoud, Elias-
dc.date.accessioned2024-06-26T10:36:28Z-
dc.date.available2024-06-26T10:36:28Z-
dc.date.created2022-07-18-
dc.date.issued2024-06-26-
dc.identifier.urihttp://hdl.handle.net/20.500.12738/15979-
dc.description.abstractDiese Arbeit beschreibt den Entwurf und das Training eines künstlichen neuronalen Netzwerks mit Keras Tensorflow high level Machine Learning framework. Dieses Training ermöglicht es dem Netzwerk, Widerstände in Eingabebildern nach ihrem Wert zu klassifizieren. Das Modell soll nach dem Training und der Auswertung gespeichert werden, um dann in diesem Projekt von einer Android-Anwendung verwendet zu werden.de
dc.description.abstractThis work describes designing and training an Artificial Neural Network with Keras Tensorflow high level Machine Learning framework. this training enables the network to classify resistors within input images according to their value. The model is to be saved after training and evaluation, in order to then be employed in this project by an Android application.en
dc.language.isoenen_US
dc.subjectArtificial Neuronsen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectImage Processingen_US
dc.subjectSupervised learningen_US
dc.subjectReinforced Learningen_US
dc.subjectTraining Neural Networksen_US
dc.subjectParameters and Hyper-parametersen_US
dc.subjectweighten_US
dc.subjectbiasen_US
dc.subjectlearning rateen_US
dc.subjectbatch sizeen_US
dc.subjectepochen_US
dc.subjectdata seten_US
dc.subjectoptimizeren_US
dc.subjectAndroiden_US
dc.subjectKünstliche Neuronenen_US
dc.subjectKünstliche Neuronale Netzeen_US
dc.subjectBildverarbeitungen_US
dc.subjectÜberwachtes Lernenen_US
dc.subjectVerstärktes Lernenen_US
dc.subjectTraining Neuronaler Netzeen_US
dc.subjectParameter und Hyper-Parameteren_US
dc.subjectGewichten_US
dc.subject.ddc600: Techniken_US
dc.subject.ddc620: Ingenieurwissenschaftenen_US
dc.titleClassification of Electrical Resistors by a Deep Learning Model embedded in an Application for Mobile Devicesen
dc.typeThesisen_US
openaire.rightsinfo:eu-repo/semantics/openAccessen_US
thesis.grantor.departmentDepartment Informations- und Elektrotechniken_US
thesis.grantor.universityOrInstitutionHochschule für Angewandte Wissenschaften Hamburgen_US
tuhh.contributor.refereeRauscher-Scheibe, Annabella-
tuhh.identifier.urnurn:nbn:de:gbv:18302-reposit-188232-
tuhh.oai.showtrueen_US
tuhh.publication.instituteDepartment Informations- und Elektrotechniken_US
tuhh.publication.instituteFakultät Technik und Informatiken_US
tuhh.type.opusBachelor Thesis-
dc.type.casraiSupervised Student Publication-
dc.type.dinibachelorThesis-
dc.type.driverbachelorThesis-
dc.type.statusinfo:eu-repo/semantics/publishedVersionen_US
dc.type.thesisbachelorThesisen_US
dcterms.DCMITypeText-
tuhh.dnb.statusdomainen_US
item.creatorOrcidSoud, Elias-
item.creatorGNDSoud, Elias-
item.advisorGNDHensel, Marc-
item.openairetypeThesis-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_46ec-
item.cerifentitytypePublications-
item.grantfulltextopen-
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