  Deep probabilistic programming languages try to combine the advantages of
deep learning with those of probabilistic programming languages. If successful,
this would be a big step forward in machine learning and programming languages.
Unfortunately, as of now, this new crop of languages is hard to use and
understand. This paper addresses this problem directly by explaining deep
probabilistic programming languages and indirectly by characterizing their
current strengths and weaknesses.
