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Learning deep

dc.contributor.editorSäfken, Benjamin
dc.contributor.editorSilbersdorff, Alexander
dc.contributor.editorWeisser, Christoph
dc.date.accessioned2020-11-11T11:31:19Z
dc.date.available2020-11-11T11:31:19Z
dc.date.issued2020de
dc.identifier.urihttps://doi.org/10.17875/gup2020-1338
dc.format.extent145de
dc.format.mediumPrintde
dc.language.isogerde
dc.language.isoengde
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/deed.de
dc.subject.ddc330
dc.titleLearning deepde
dc.title.alternativePerspectives on Deep Learning Algorithms and Artificial Intelligencede
dc.typeanthologyde
dc.price.print25,00
dc.identifier.urnurn:nbn:de:gbv:7-isbn-978-3-86395-462-8-6
dc.description.printSoftcover, 17x24de
dc.subject.divisionsurveyedde
dc.relation.isbn-13978-3-86395-462-8
dc.identifier.articlenumber8102081de
dc.identifier.internisbn-978-3-86395-462-8de
dc.subject.bisacBUS000000
dc.subject.vlb780
dc.subject.bicK
dc.description.abstractengArtificial intelligence is considered to be one of the most decisive topics in the 21th century. Deep learning algorithms, which are the basis of artificial intelligence applications, are of central interest for researchers but also for students that strive to build up academic knowledge and practical competences in this field. The Deep Learning Seminar at the University of Göttingen follows the central notion of the Humboldtian model of higher education and offers graduate students of applied statistics the opportunity to conduct their own research. The quality of the results motivated us to publish the most promising seminar papers in this volume. For the selected papers a full peer review process was conducted. The presented contributions cover a broad range of deep learning topics. The articles in the first part of this volume may serve the reader as introduction to deep learning algorithms. Subsequently, research applications allow the reader to gain deep insights into some of the latest developments in the field of artificial intelligence.de
dc.notes.vlb-printlieferbar
dc.intern.doi10.17875/gup2020-1338de
dc.identifier.purlhttp://resolver.sub.uni-goettingen.de/purl?univerlag-isbn-978-3-86395-462-8
dc.format.chapters-de


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