  Recent advances in statistical inference have significantly expanded the
toolbox of probabilistic modeling. Historically, probabilistic modeling has
been constrained to (i) very restricted model classes where exact or
approximate probabilistic inference were feasible, and (ii) small or
medium-sized data sets which fit within the main memory of the computer.
However, developments in variational inference, a general form of approximate
probabilistic inference originated in statistical physics, are allowing
probabilistic modeling to overcome these restrictions: (i) Approximate
probabilistic inference is now possible over a broad class of probabilistic
models containing a large number of parameters, and (ii) scalable inference
methods based on stochastic gradient descent and distributed computation
engines allow to apply probabilistic modeling over massive data sets. One
important practical consequence of these advances is the possibility to include
deep neural networks within a probabilistic model to capture complex non-linear
stochastic relationships between random variables. These advances in
conjunction with the release of novel probabilistic modeling toolboxes have
greatly expanded the scope of application of probabilistic models, and allow
these models to take advantage of the recent strides made by the deep learning
community. In this paper we review the main concepts, methods and tools needed
to use deep neural networks within a probabilistic modeling framework.
