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In ultra-dense heterogeneous networks, caching popular contents at small base stations is considered as an effective way to reduce latency and redundant data transmission. Optimization of caching placement/replacement and content delivering can be computationally heavy, especially for large-scale networks. The provision of both time-efficient and high-quality solutions is challenging. Conventional iterative optimization methods, either optimal or heuristic, typically require a large number of iterations to achieve satisfactory performance, and result in considerable computational delay. This may limit their applications in practical network operations where online decisions have to be made. In this paper, we provide a viable alternative to the conventional methods for caching optimization, from a deep learning perspective. The idea is to train the optimization algorithms through a deep neural network (DNN) in …