Workflow for Neural Network Design

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.