- Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained.
- They are used to define the number of hidden layers that must be present in a network.
- More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause underfitting.