  Nowadays, methods and techniques of Machine Learning and Deep Learning are
being used in various scientific areas. They help to automatize calculations
without losing in quality. In this paper the applying of convolutional neural
network was considered in frame of problems from statistical physics and
computer simulation of magnetic films. In a frame of the first task, CNN was
used to determine critical Curie point for Ising model on 2D square lattice.
Obtained results were compared with classical Monte-Carlo methods and exact
solution. Systems of various lattice sizes and the influence of the size effect
on the results' accuracy were considered. Also, authors considered the
classical two-dimensional Heisenberg model, a spin system with direct
short-range exchange, and studied of its competition with the
Dzyaloshinskii-Moriya interaction. A neural network was applied to the
recognition of Spiral (Sp), Spiral-skyrmion (SpSk) Skyrmion (Sk),
Skyrmion-ferromagnetic (SkF) and Ferromagnetic (FM) phases of the Heisenberg
spin system with magnetic skyrmions. The advantage of CNN's application over
conventional methods for determination of skyrmion's phases was revealed.
