A neural network’s primary function is to receive a set of inputs, perform progressively complex computations, and then use the output to SOLVE the problem. Neural networks are used for lot of different applications, one example would be classification. There are lots of classifiers AVAILABLE today such as logistic regression, support vector machine, decision trees, random forest and so on and of COURSE neural networks.

For example, say we needed to predict if a person is healthy or sick. All you have are some input information such as height, weight, body temperature of each person, there is a need to classify / predict if a person is sick or healthy is a classification problem and it can be SOLVED using approaches such as neural networks. The classifier would receive the data about the patient, process it and give a CONFIDENCE score. A high score would indicate high confidence that patient is sick and a low score would suggest they are healthy. Score could be probability value of 0 to 1.

Neural network is highly structured and comes in layers. First layer is the input layer, last layer is the output layer and all layers in between are referred to as hidden layers. Hence a neural network can be viewed as the result of spinning classifiers together in a layered web.