CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
Year: 2017
Type: conference-paper
Abstract: The role of neural network modeling in the learning сontent of spe- cial course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT- specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. Co- Calc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using Co- Calc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic compo- nents of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of cal- culating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the archi- tecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
Authors: Oksana Markova, Serhiy Semerikov, Maiia Popel
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W2810013249