  We show that a neural network, trained on the entanglement spectra of a
nearest neighbor Heisenberg chain in a random transverse magnetic field, can be
used to efficiently study the ergodic/many-body localized properties of a
number of other quantum systems, without further re-training. We benchmark our
computational architecture against a $J_{1}\!\!-\!\!J_{2}$ - model, which
extends the Heisenberg chain to include next-to-nearest neighbor interactions,
with excellent agreement with known results. When applied to Hamiltonians that
differ from the training model by the topology of the underlying graph, the
neural network is able to predict the critical disorders and, more generally,
shapes of the mobility edge that agree with our heuristic expectation. We take
this as proof of principle that machine learning algorithms furnish a powerful
new set of computational tools to explore the many-body physics frontier.
