  The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for caching
