Deep learning models are becoming very popular. They have very deep roots in the way biological neurons are connected and the way they transmit information from one node to another node in a network model.
Deep learning has a very specific usage, particularly when the single function–based machine learning techniques fail to approximate real-life challenges. For example, when the data dimension is very large (in the thousands), then standard machine learning algorithms fail to predict or classify the outcome variable. This is also not very efficient computationally. ...
