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Please use this identifier to cite or link to this item: http://ds.knu.edu.ua/jspui/handle/123456789/2200
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dc.contributor.authorTarasenko, Andrii-
dc.contributor.authorYakimov, Yuriy-
dc.contributor.authorSoloviev, Vladimir-
dc.date.accessioned2020-04-15T06:02:28Z-
dc.date.available2020-04-15T06:02:28Z-
dc.date.issued2020-
dc.identifier.citationTarasenko Andrii Convolutional neural networks for image classification [Electronic resource] / Andrii Tarasenko, Yuriy Yakimov, Vladimir Soloviev // Computer Science & Software Engineering : Proceedings of the 2nd Student Workshop (CS&SE@SW 2019), Kryvyi Rih, Ukraine, November 29, 2019 / edited by : Arnold Kiv, Serhiy Semerikov, Vladimir Soloviev, Andrii Striuk. – P. 101–114. – (CEUR Workshop Proceedings (CEUR-WS.org), vol. 2546). – Access mode : http://ceur-ws.org/Vol-2546/paper06.pdf.-
dc.identifier.issn1613-0073-
dc.identifier.urihttp://ceur-ws.org/Vol-2546/paper06.pdf-
dc.identifier.urihttp://ds.knu.edu.ua/jspui/handle/123456789/2200-
dc.descriptionThis paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculation of recognition error and training accuracy are presented.-
dc.language.isoenuk_UA
dc.subjectmachine learninguk_UA
dc.subjectdeep learninguk_UA
dc.subjectneural networkuk_UA
dc.subjectrecognitionuk_UA
dc.subjectconvolutional neural networkuk_UA
dc.subjectartificial intelligenceuk_UA
dc.titleConvolutional neural networks for image classificationuk_UA
dc.typeArticleuk_UA
local.submitter.emailsemerikov@ccjourn...uk_UA
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