Hybrid analog/digital processing is crucial for millimeter-wave (mmWave) MIMO systems, due to its ability to balance the gain and cost. Despite fruitful recent studies, the optimal beamforming/combining method remains unknown for a practical multiuser, broadband mmWave MIMO equipped with low-resolution phase shifters and low-resolution analog-to-digital converters (ADCs).In this paper, we leverage artificial intelligence techniques to tackle this problem. Particularly, we propose a neural hybrid beamforming/combining (NHB) MIMO system, where the various types of hybrid analog/digital mmWave MIMO systems are transformed into a corresponding autoencoder (AE) based neural networks. Consequently, the digital and analog beamformers/combiners are obtained by training the AE based new model in an unsupervised learning manner, regardless of particular configurations. Using this approach, we can apply amachine learning-based design methodology that is compatible with a range of different beamforming/combing architectures. We also propose an iterative training strategy for the neural network parameter updating, which can effective guarantee fast convergence of the established NHB model. According to simulation results, the proposed NHB can offer a significant performance gain over existing methods in term of bit