SHS Web Conf.
Volume 75, 2020The International Conference on History, Theory and Methodology of Learning (ICHTML 2020)
|Number of page(s)||14|
|Section||Methodology of Learning, Education and Training|
|Published online||26 March 2020|
Using spreadsheets as learning tools for computer simulation of neural networks
1 Kryvyi Rih State Pedagogical University,54, Gagarina Ave, Kryvyi Rih, 50086, Ukraine
2 Kryvyi Rih National University,11, Vitali Matusevich St., Kryvyi Rih, 50027, Ukraine
3 Ben-Gurion University of the Negev, Beer Sheva, Israel
* Corresponding author:firstname.lastname@example.org
The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment with-out add-ins and macros. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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