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Title:	Convolutional neural networks for image classification
Authors:	Tarasenko, Andrii Yakimov, Yuriy Soloviev, Vladimir
Keywords:	machine learning deep learning neural network recognition convolutional neural network artificial intelligence
Issue Date:	2020
Citation:	Tarasenko 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.
Description:	This 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.
URI:	http://ceur-ws.org/Vol-2546/paper06.pdf http://ds.knu.edu.ua/jspui/handle/123456789/2200
ISSN:	1613-0073
Appears in Collections:	Наукові статті

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