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Head pose estimation is an old problem that is recently receiving new attention because of possible applications in human-robot interaction, augmented reality and driving assistance. However, most of the existing work has been tested in controlled environments and is not robust enough for real-world applications. In order to handle these limitations we propose an approach based on Convolutional Neural Networks (CNNs) supplemented with the most recent techniques adopted from the deep learning community. We evaluate the performance of four architectures on recently released in-the-wild datasets. Moreover, we investigate the use of dropout and adaptive gradient methods giving a contribution to their ongoing validation. The results show that joining CNNs and adaptive gradient methods leads to the state-of-the-art in unconstrained head pose estimation.