  Neural networks can be used to identify phases and phase transitions in
condensed matter systems via supervised machine learning. Readily programmable
through modern software libraries, we show that a standard feed-forward neural
network can be trained to detect multiple types of order parameter directly
from raw state configurations sampled with Monte Carlo. In addition, they can
detect highly non-trivial states such as Coulomb phases, and if modified to a
convolutional neural network, topological phases with no conventional order
parameter. We show that this classification occurs within the neural network
without knowledge of the Hamiltonian or even the general locality of
interactions. These results demonstrate the power of machine learning as a
basic research tool in the field of condensed matter and statistical physics.
