  Deep learning has solved a problem that as little as five years ago was
thought by many to be intractable - the automatic recognition of patterns in
data; and it can do so with accuracy that often surpasses human beings. It has
solved problems beyond the realm of traditional, hand-crafted machine learning
algorithms and captured the imagination of practitioners trying to make sense
out of the flood of data that now inundates our society. As public awareness of
the efficacy of DL increases so does the desire to make use of it. But even for
highly trained professionals it can be daunting to approach the rapidly
increasing body of knowledge produced by experts in the field. Where does one
start? How does one determine if a particular model is applicable to their
problem? How does one train and deploy such a network? A primer on the subject
can be a good place to start. With that in mind, we present an overview of some
of the key multilayer ANNs that comprise DL. We also discuss some new automatic
architecture optimization protocols that use multi-agent approaches. Further,
since guaranteeing system uptime is becoming critical to many computer
applications, we include a section on using neural networks for fault detection
and subsequent mitigation. This is followed by an exploratory survey of several
application areas where DL has emerged as a game-changing technology: anomalous
behavior detection in financial applications or in financial time-series
forecasting, predictive and prescriptive analytics, medical image processing
and analysis and power systems research. The thrust of this review is to
outline emerging areas of application-oriented research within the DL community
as well as to provide a reference to researchers seeking to use it in their
work for what it does best: statistical pattern recognition with unparalleled
learning capacity with the ability to scale with information.
