  Although deep learning techniques have been successfully applied to many
tasks, interpreting deep neural network models is still a big challenge to us.
Recently, many works have been done on visualizing and analyzing the mechanism
of deep neural networks in the areas of image processing and natural language
processing. In this paper, we present our approaches to visualize and
understand deep neural networks for a very important commercial task--CTR
(Click-through rate) prediction. We conduct experiments on the productive data
from our online advertising system with daily varying distribution. To
understand the mechanism and the performance of the model, we inspect the
model's inner status at neuron level. Also, a probe approach is implemented to
measure the layer-wise performance of the model. Moreover, to measure the
influence from the input features, we calculate saliency scores based on the
back-propagated gradients. Practical applications are also discussed, for
example, in understanding, monitoring, diagnosing and refining models and
algorithms.
