Resource allocation algorithms in wireless networks can require solving complex optimization problems at every decision epoch. For large scale networks, when decisions need to be taken on time scales of milliseconds, using standard convex optimization solvers for computing the optimum can be a time-consuming affair that may impair real-time decision making. In this paper, we propose to use Data-driven and Deep Feedforward Neural Networks (DFNN) for learning the relation between the inputs and the outputs of two such resource allocation algorithms that were proposed in [1, 2]. On numerical examples with realistic mobility patterns, we show that the learning algorithm yields an approximate yet satisfactory solution with much less computation time.