  Machine learning is becoming widely used in analyzing the thermodynamics of
many-body condensed matter systems. Restricted Boltzmann Machine (RBM) aided
Monte Carlo simulations have sparked interest recently, as they manage to speed
up classical Monte Carlo simulations. Here we employ the Convolutional
Restricted Boltzmann Machine (CRBM) method and show that its use helps to
reduce the number of parameters to be learned drastically by taking advantage
of translation invariance. Furthermore, we show that it is possible to train
the CRBM at smaller lattice sizes, and apply it to larger lattice sizes. To
demonstrate the efficiency of CRBM we apply it to the paradigmatic Ising and
Kitaev models in two-dimensions.
