  In the last couple of decades, tremendous progress has been achieved in
developing robotic telescopes and, as a result, sky surveys (both terrestrial
and space) have become the source of a substantial amount of new observational
data. These data contain a lot of information about binary stars, hidden in
their light curves. With the huge amount of astronomical data gathered, it is
not reasonable to expect all the data to be manually processed and analyzed.
Therefore, in this paper, we focus on the automatic classification of eclipsing
binary stars using deep learning methods. Our classifier provides a tool for
the categorization of light curves of binary stars into two classes: detached
and over-contact. We used the ELISa software to obtain synthetic data, which we
then used for the training of the classifier. For evaluation purposes, we
collected 100 light curves of observed binary stars, in order to evaluate a
number of classifiers. We evaluated semi-detached eclipsing binary stars as
detached. The best-performing classifier combines bidirectional Long Short-Term
Memory (LSTM) and a one-dimensional convolutional neural network, which
achieved 98% accuracy on the evaluation set. Omitting semi-detached eclipsing
binary stars, we could obtain 100% accuracy in classification.
