  We consider a machine learning algorithm to detect and identify strong
gravitational lenses on sky images. First, we simulate different artificial but
very close to reality images of galaxies, stars and strong lenses, using six
different methods, i.e. two for each class. Then we deploy a convolutional
neural network architecture to classify these simulated images. We show that
after neural network training process one achieves about 93 percent accuracy.
As a simple test for the efficiency of the convolutional neural network, we
apply it on an real Einstein cross image. Deployed neural network classifies it
as gravitational lens, thus opening a way for variety of lens search
applications of the deployed machine learning scheme.
