  We report an experimental demonstration of a machine learning approach to
identify exotic topological phases, with a focus on the three-dimensional
chiral topological insulators. We show that the convolutional neural
networks---a class of deep feed-forward artificial neural networks with
widespread applications in machine learning---can be trained to successfully
identify different topological phases protected by chiral symmetry from
experimental raw data generated with a solid-state quantum simulator. Our
results explicitly showcase the exceptional power of machine learning in the
experimental detection of topological phases, which paves a way to study rich
topological phenomena with the machine learning toolbox.
