In this section, we will discuss its architecture and address several of the common tasks performed when using the API. DLN typically starts with the creation of a MultiLayerConfiguration
instance, which defines the network, or model. The network is composed of multiple layers. Hyperparameters are used to configure the network and are variables that affect such things as learning speed, activation functions to use for a layer, and how weights are to be initialized.
As with neural networks, the basic DLN process consists of:
Acquiring and manipulating data
Configuring and building a model
Training the model
Testing the model
We will investigate each of these tasks in the next sections.