Computer networks are dealing with growing complexity, given the ever-increasing volume of data produced by all sorts of network nodes. Performance improvements are a non-stop ambition and require tuning fine-grained details of the system operation. Analyzing such data deluge, however, is not straightforward and sometimes not supported by the system. There are often problems regarding scalability and the predisposition of the involved nodes to understand and transfer the data. This issue is at least partially circumvented by knowledge acquisition from past experiences, which is a characteristic of the herein called ‘‘challenged networks’’. The addition of intelligence in these scenarios is fundamental to extract linear and non-linear relationships from the data collected by multiple sources. This is undoubtedly an invitation to machine learning and, more particularly, to deep learning.