The knowledge flows along an extensive sequence of layers. As humans, the information is learnt step by step. First layers focus on learning more specific concepts while the deeper layers will use the information already learnt to soak in more abstract concepts. This procedure of constructing representations of the data is known as feature extraction.

Their complex architecture provides deep neural nets with the ability to perform a feature extraction automatically. On the contrary, in conventional machine learning, or shallow learning, this task is carried out outside the algorithmic stage. People, data scientists’ teams and not machines, are in charge of analyzing raw data and change it into valuable features. The fundamental advantage of Deep Learning is that these algorithms can be trained on unstructured data, with unlimited access to info. And this powerful condition provides them the opportunity to obtain more profitable learning.