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dc.contributor.advisorMorgül, Ömer
dc.contributor.authorÇatalbaş, Burak
dc.date.accessioned2018-09-13T09:21:44Z
dc.date.available2018-09-13T09:21:44Z
dc.date.copyright2018-09
dc.date.issued2018-09
dc.date.submitted2018-09-11
dc.identifier.urihttp://hdl.handle.net/11693/47866
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 111-115).en_US
dc.description.abstractArtificial Neural Networks (ANNs) are used for different machine learning tasks such as classification, clustering etc. They have been utilized in important tasks and offering new services more and more in our daily lives. Learning capabilities of these networks have accelerated significantly since 2000s, with the increasing computational power and data amount. Therefore, research conducted on these networks is renamed as Deep Learning, which emerged as a major research area - not only in the neural networks, but also in the Machine Learning discipline. For such an important research field, the techniques used in the training of these networks can be seen as keys for more successful results. In this work, each part of this training procedure is investigated by using of different and improved - sometimes new - techniques on convolutional neural networks which classify grayscale and colored image datasets. Advanced methods included the ones from the literature such as He-truncated Gaussian initialization. In addition, our contributions to the literature include ones such as SinAdaMax Optimizer, Dominantly Exponential Linear Unit (DELU), He-truncated Laplacian initialization and Pyramid Approach for Max-Pool layers. In the chapters of this thesis, success rates are increased with the addition of these advanced methods accumulatively, especially with DELU and SinAdaMax which are our contributions as upgraded methods. In result, success rate thresholds for different datasets are met with simple convolutional neural networks - which are improved with these advanced methods and reached promising test success increases - within 15 to 21 hours (typically less than a day). Thus, better performances are obtained by those different and improved techniques are shown using well-known classification datasets.en_US
dc.description.statementofresponsibilityby Burak Çatalbaş.en_US
dc.format.extentxvii, 115 leaves : illustrations, charts (some color) ; 30 cm.en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArti cial Neural Networksen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDeep Learningen_US
dc.subjectNeural Network Trainingen_US
dc.subjectCIFAR-10en_US
dc.subjectMNISTen_US
dc.titleImproved artificial neural network training with advanced methodsen_US
dc.title.alternativeİleri yöntemlerle geliştirilmiş yapay sinir ağı eğitimien_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158935


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