The work flow for the neural network design process has seven primary steps:
This topic discusses the basic ideas behind steps 2, 3, 5, and 7. The details of these steps come in later topics, as do discussions of steps 4 and 6, since the fine points are specific to the type of network that you are using. (Data collection in step 1 generally occurs outside the framework of Neural Network Toolbox™ software, but it is discussed in Multilayer Neural Networks and Backpropagation Training.)
The Neural Network Toolbox software uses the network object to store all of the information that defines a neural network. This topic describes the basic components of a neural network and shows how they are created and stored in the network object.
After a neural network has been created, it needs to be configured and then trained. Configuration involves arranging the network so that it is compatible with the problem you want to solve, as defined by sample data. After the network has been configured, the adjustable network parameters (called weights and biases) need to be tuned, so that the network performance is optimized. This tuning process is referred to as training the network. Configuration and training require that the network be provided with example data. This topic shows how to format the data for presentation to the network. It also explains network configuration and the two forms of network training: incremental training and batch training.