Artificial Neural Network

Artificial Neural Network is an information processing paradigm which is used to study the behaviour of a complex system by computer simulation. It is inspired by the biological way of processing information by the human brain. The key element of the paradigm is the novel structure of the information processing system.  The goal of the artificial neural network is to solve any specific problem in the same way as a human brain would.

Artificial Neural Networks consists of multiple nodes which mimic the biological neurons of a human brain. These nodes interact by taking the data and performing operations on it and then passing it over to the other connected node in the link. Neural networks are organized in layers. Layers are made up of interconnected nodes. Patterns are presented to the network via the ‘input layer’, which links to one or more ‘hidden layers’ where the actual processing is done via a system of weighted connections. The hidden layer then links to an ‘output layer’.  Typically, a neural network is initially trained. Training consists of providing the input and telling the network what the output should be.

Example:

To identify the faces, initial training might be a series of pictures of men, women, masks,  animal faces and so on. Each input is accompanied by the matching identification, such as “men”, "women" or "not human" information. Providing the answers allows the model to adjust its internal weightings to learn how to do its job better.