The 5 Kinds of Problems where Deep Learning is Applicable

What's not apparent to many practitioners and researchers in Deep Learning is that the rich variety of methods developed over the past several years are relevant to systems that have different kinds of goals. Deep Learning arose from the Machine Learning community, so it is natural to think of DL networks as systems suitable for performing predictions. Predictions are unfortunately too broad a goal and thus leads to a lack of specificity as to the appropriate methods to fine tune a solution. What I mean here is that you can cast almost any intelligent goals as that of making a prediction. However, to be successful, one has to a minimum understand what kind of prediction is being made and this leads towards a more pragmatic understanding of whether the right tools for the job are used.