  We train a set of Restricted Boltzmann Machines (RBMs) on one- and
two-dimensional Ising spin configurations at various values of temperature,
generated using Monte Carlo simulations. We validate the training procedure by
monitoring several estimators, including measurements of the log-likelihood,
with the corresponding partition functions estimated using annealed importance
sampling. The effects of various choices of hyper-parameters on training the
RBM are discussed in detail, with a generic prescription provided. Finally, we
present a closed form expression for extracting the values of couplings, for
every $n$-point interaction between the visible nodes of an RBM, in a binary
system such as the Ising model. We aim at using this study as the foundation
for further investigations of less well-known systems.
