Open AccessScientific personnel training in convolutional neural networks for the implementation of research projects of the MegaScience class
Author(s) -
Alexander Shtanko,
Sergey Kulik
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1406/1/012014
Subject(s) - computer science , convolutional neural network , class (philosophy) , artificial neural network , artificial intelligence , training (meteorology) , management science , engineering , physics , meteorology
Megascience projects require an all-inclusive interdisciplinary approach. Because of that scientific personnel engaged in projects of this type must possess relevant required interdisciplinary skills. Artificial intelligence in particular convolutional neural networks has a wide range of applications, and it could be used to solve complicated problems in all kinds of various fields of science. Thus, the understanding of the principles of neural networks’ working is a necessary skill for scientific personnel. In this paper, we’re considering practical examples and fields of applications of neural networks in the real world.