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. It is majorly responsible for learning the data representation and for extracting the features.

There can be any number of hidden layers; however, we have to choose a number of hidden layers according to our use case. For a very simple problem, we can just use one hidden layer, but while performing complex tasks such as image recognition, we use many hidden layers, where each layer is responsible for extracting important features. The network is called a deep neural network when we have many hidden layers.