6 Deep Learning Models -- When should you use them?

Deep Learning is a growing field with applications that span across a number of use cases. For anyone new to this field, it is important to know and understand the different types of models used in Deep Learning. In this article, I'll explain each of the following models: There are a number of features that distinguish the two, but the most integral point of difference is in how these models are trained. While supervised models are trained through examples of a particular set of data, unsupervised models are only given input data and don't have a set outcome they can learn from. So that y-column that we're always trying to predict is not there in an unsupervised model.