Learning deep

dc.contributor.editor	Säfken, Benjamin
dc.contributor.editor	Silbersdorff, Alexander
dc.contributor.editor	Weisser, Christoph
dc.date.accessioned	2020-11-11T11:31:19Z
dc.date.available	2020-11-11T11:31:19Z
dc.date.issued	2020	de
dc.identifier.uri	https://doi.org/10.17875/gup2020-1338
dc.format.extent	145	de
dc.format.medium	Print	de
dc.language.iso	ger	de
dc.language.iso	eng	de
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/deed.de
dc.subject.ddc	330
dc.title	Learning deep	de
dc.title.alternative	Perspectives on Deep Learning Algorithms and Artificial Intelligence	de
dc.type	anthology	de
dc.price.print	25,00
dc.identifier.urn	urn:nbn:de:gbv:7-isbn-978-3-86395-462-8-6
dc.description.print	Softcover, 17x24	de
dc.subject.division	surveyed	de
dc.relation.isbn-13	978-3-86395-462-8
dc.identifier.articlenumber	8102081	de
dc.identifier.intern	isbn-978-3-86395-462-8	de
dc.subject.bisac	BUS000000
dc.subject.vlb	780
dc.subject.bic	K
dc.description.abstracteng	Artificial 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-print	lieferbar
dc.intern.doi	10.17875/gup2020-1338	de
dc.identifier.purl	http://resolver.sub.uni-goettingen.de/purl?univerlag-isbn-978-3-86395-462-8
dc.format.chapters	-	de
dc.identifier.asin	3863954629

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