Neural Networks are modeled after the neurons in the human brain. It comprises many layers of neurons that are structured to transmit information from the input layer to the output layer. Between the input and the output layer, there are hidden layers present.
These hidden layers can be many or just one. A simple neural network comprising of a single hidden layer is known as Perceptron.
In the above diagram for a simple neural network, there is an input layer that takes the input in the form of a vector. Then, this input is passed to the hidden layer which comprises of various mathematical functions that perform computation on the given input.
For example, given the images of cats and dogs, our hidden layers perform various mathematical operations to find the maximum probability of the class our input image falls in. This is an example of binary classification where the class, that is, dog or cat, is assigned its appropriate place.