r- vised learning has only access to the observations or historical data. Clustering is commonly used in unsupervised learning, where the goal is to separate data into "similar" groups based on a given set of attributes. An example is clustering images of people, which may lead to groups that have similar faces, identity, backgrounds, clothing, etc. Regressions and classification are commonly used methods for supervised learning. Regressions enable predictive analytics of continuous data, while classification enables predictive analytics of data into discrete or finite groups. Supervised learning usually results in better performance than unsupervised learning, although it requires extensive labeling of data to identify the "truth" state. It is commonly used in speech recognition although it requires audio files to be manually transcribed. Semi-supervised techniques provide a tradeoff between supervised and unsupervised learning. It involves labeling a subset of data that is used to create an initial model; applying the model to label a new data set, which is then included in training the supervised model. The process continues until all data is employed in training. Building the Network of the Future In the Network Cloud, supervised, semisupervised, and unsupervised learning are very relevant for closed-loop automation as will be shown in a later section. These methods are also relevant for 5G and next-generation access for traffic optimization. An excellent overview of supervised and unsupervised training can be found in Ng's Coursera course on ML [Ng16]. 8.2.5.3 Deep Learning The basic premise of more recent research in ML, referred to as deep learning, is that you can train a large number of nonlinear processing units with deep layers of the so-called neurons to learn any translation of data or patterns. This has attracted practitioners in areas of web search, language translation and chatbots, among others. The premise to transform and process arbitrary data, has sig- nificant impact to business applications