  Machine learning has played an important role in the analysis of high-energy
physics data for decades. The emergence of deep learning in 2012 allowed for
machine learning tools which could adeptly handle higher-dimensional and more
complex problems than previously feasible. This review is aimed at the reader
who is familiar with high energy physics but not machine learning. The
connections between machine learning and high energy physics data analysis are
explored, followed by an introduction to the core concepts of neural networks,
examples of the key results demonstrating the power of deep learning for
analysis of LHC data, and discussion of future prospects and concerns.
