Convolutional neural networks for image classification

- Tarasenko, Andrii O., Yakimov, Yuriy V. and Soloviev, V.N. (orcid.org/0000-0002-4945-202X) (2019) Convolutional neural networks for image classification Computer Science & Software Engineering : Proceedings of the 2nd Student Workshop (CS&SE@SW 2019), Kryvyi Rih, Ukraine, November 29, 2019 (2546). pp. 101-114. ISSN 1613-0073

[img] Text
paper06.pdf - Published Version

Download (3MB)

Abstract

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.

Item Type: Article
Keywords: machine learning, deep learning, neural network, recognition, convolutional neural network, artificial intelligence
Subjects: Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques )
Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 378 Higher education. Universities. Academic study
Divisions: Information Technologies and Learning Tools > Joint laboratory with SIHE “Kryvyi Rih National University”
Depositing User: Сергій Олексійович Семеріков
Date Deposited: 24 Apr 2020 20:53
Last Modified: 24 Apr 2020 20:53
URI: http://lib.iitta.gov.ua/id/eprint/720099

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item