  Deep learning, a branch of machine learning, have been recently applied to
high energy experimental and phenomenological studies. In this note we give a
brief review on those applications using supervised deep learning. We first
describe various learning models and then recapitulate their applications to
high energy phenomenological studies. Some detailed applications are delineated
in details, including the machine learning scan in the analysis of new physics
parameter space, the graph neural networks in the search of top-squark
production and in the $CP$ measurement of the top-Higgs coupling at the LHC.
