  Deep Learning methods have been adopted in mobile networks, especially for
network management automation where they provide means for advanced machine
cognition. Deep learning methods utilize cutting-edge hardware and software
tools, allowing complex cognitive algorithms to be developed. In a recent
paper, we introduced the Bounding Sphere Quantization (BSQ) algorithm, a
modification of the k-Means algorithm, that was shown to create better
quantizations for certain network management use-cases, such as anomaly
detection. However, BSQ required a significantly longer time to train than
k-Means, a challenge which can be overcome with a neural network-based
implementation. In this paper, we present such an implementation of BSQ that
utilizes state-of-the-art deep learning tools to achieve a competitive training
speed.
