What is a neural network used for?#
It automates the discovery of these complex rules and relationships by learning directly from vast amounts of example data, enabling tasks like accurate prediction, classification, and recognition at a massive scale.
How does a neural network work?#
A neural network is "trained" by being fed huge amounts of labelled data. For example, to build a system that identifies high-risk loan applications, it is shown thousands of past applications labelled as "defaulted" or "paid in full." Initially, its guesses are random, but with each example, it slightly adjusts its internal connections to improve the accuracy of its predictions.
Through thousands or millions of these tiny adjustments, the network effectively builds its own internal logic for what constitutes a risky application. Once this training is complete, the network can be presented with a brand new application it has never seen before and make a highly accurate prediction about its potential risk.
How is a neural network different from rule-based software?#
A neural network learns patterns from data. In contrast, traditional software is built on rules explicitly programmed by a human developer. For example, to filter customer support tickets, a traditional program might have a rule like "If the subject contains 'urgent,' assign high priority," whereas a neural network learns what an urgent request looks like by analysing thousands of examples of past tickets and their outcomes.