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Metahevristic methods for neural networks optimisation

Student: Evgeniy Khomutov

Supervisor: Alexey Timofeev

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2019

This paper explores the use of metaheuristic optimization algorithms for tuning weights in neural networks and improving machine learning algorithms. Approbation of the approach on linear and nonlinear neural network models is considered. The features of this approach to use in generative-competitive networks are also considered. The purpose of this final work is to identify the problems and advantages of using this approach to optimizing neural networks.

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