  These notes were compiled as lecture notes for a course developed and taught
at the University of the Southern California. They should be accessible to a
typical engineering graduate student with a strong background in Applied
Mathematics.
  The main objective of these notes is to introduce a student who is familiar
with concepts in linear algebra and partial differential equations to select
topics in deep learning. These lecture notes exploit the strong connections
between deep learning algorithms and the more conventional techniques of
computational physics to achieve two goals. First, they use concepts from
computational physics to develop an understanding of deep learning algorithms.
Not surprisingly, many concepts in deep learning can be connected to similar
concepts in computational physics, and one can utilize this connection to
better understand these algorithms. Second, several novel deep learning
algorithms can be used to solve challenging problems in computational physics.
Thus, they offer someone who is interested in modeling a physical phenomena
with a complementary set of tools.
