Any layer between the input layer and the output layer is called a hidden layer. It processes the input received from the input layer. The hidden layer is responsible for deriving complex relationships between input and output. That is, the hidden layer identifies the pattern in the dataset. There can be any number of hidden layers, however we have to choose a number of hidden layers according to our problem. For a very simple problem, we can just use one hidden layer, but while performing complex tasks like image recognition, we use many hidden layers where each layer is responsible for extracting important features of the image so that we can easily recognize the image. When we use many hidden layers, the network is called a deep neural network.