  The classical XY model is a lattice model of statistical mechanics notable
for its universality in the rich hierarchy of the optical, laser and condensed
matter systems. We show how to build complex structures for machine learning
based on the XY model's nonlinear blocks. The final target is to reproduce the
deep learning architectures, which can perform complicated tasks usually
attributed to such architectures: speech recognition, visual processing, or
other complex classification types with high quality. We developed the robust
and transparent approach for the construction of such models, which has
universal applicability (i.e. does not strongly connect to any particular
physical system), allows many possible extensions while at the same time
preserving the simplicity of the methodology.
