Deep Networks

Deep neural network (DNN) is a subset of Artificial Neural Network (ANN) comprised of multiple hidden layers between input and output layers. Moreover, the main purpose of artificial neural network is to receive a set of inputs, perform progressively complex calculations on them and give possible output to solve complex real world problems in an enterprise. Deep neural network therefore focuses on these very complex tasks that have large input data and high dimensionality. And it is highly applicable in machine learning and deep learning algorithms to solve complex computer vision problems.

The difference between deep and shallow networks is that shallow networks refers to a neural networks that a small number of layers, which is mostly regarded as having a single hidden layer. Whereas deep networks refer to neural networks that have multiple hidden layers. Besides, the terms shallow and deep refers to the number of layers in an artificial neural networks (ANN). Furthermore, both types of ANN perform certain tasks better than the other depending on the requirements. However, similar to shallow artificial neural networks, deep neural networks can model complex non-leaner relationships