The ANN has a layered approach. It has one input layer and one output layer. There may be one or more hidden layers. When the constructed network becomes deep (with a higher number of hidden layers), it is called a deep network or deep learning network. The popularity of deep learning can be understood from the fact that leading IT companies like Google, Facebook, Microsoft and Baidu have invested in it in domains like speech, image and behaviour modelling. Deep learning has various characteristics as listed below: In the traditional approach, the features of machine learning or shallow learning have to be identified by humans whereas in deep learning, the features are not human constructed. Deep learning involves ANN with a greater number of layers. Deep learning handles end-to-end compositional models. The hierarchy of representations with different data is handled. For example, with speech, the hierarchical representation is: Audio -> Band -> Phone -> Word. vision, it is: Pixel -> Motif -> Part -> Object. There are plenty of resources available on the Web to understand deep learning. However, at times it leads to the problem of plenty. The lecture delivered by Dr Andrew Ng at the GPU Technology Conference 2015 is one of the best to start with, in order to get a clear understanding of deep learning and its potential applications ( http://www.ustream.tv/ recorded/60113824).