The work flow for the neural network design process has seven primary steps. Referenced topics discuss the basic ideas behind steps 2, 3, and 5.
Data collection in step 1 generally occurs outside the framework of Deep Learning Toolbox™ software, but it is discussed in general terms in Multilayer Shallow Neural Networks and Backpropagation Training. Details of the other steps and discussions of steps 4, 6, and 7, are discussed in topics specific to the type of network.
The Deep Learning 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.