What are neural networks and how do they work?

 Neural networks find patterns and relationships in very large amounts of data that would be too complicated and difficult for a human being to analyze by using machine learning algorithms and computational models that emulate the processing patterns of the biological human brain. A neural network has a large number of sensing and processing nodes that continuously interact with each other. Neural networks are comprised of an input layer, a hidden processing layer, and an output layer. Humans train the network by feeding it a set of training data for which the inputs produce a known set of outputs or conclusions. This helps the computer learn the correct solution by example. Neural network applications in medicine, science, and business address problems in pattern classification, prediction, and control and optimization.