Neural networks constitute multiple parameters that can affect the ultimate accuracy in predicting an event or a label. The typical parameters include:

• Batch size used for training
• Number of epochs
• Learning rate
• Number of hidden layers
• Number of hidden units in each hidden layer
• The activation function applied in the hidden layer
• The optimizer used

From the preceding list, we can see that the number of parameters that can be tweaked is very high. This makes finding the optimal combination of hyperparameters a challenge. Hyperparameter tuning as a service provided by Cloud ML Engine comes in handy in such a scenario.

In this chapter, we will go through:

• Why hyperparameter tuning is required
• An overview of how hyperparameter tuning works
• Implementing hyperparameter tuning in the cloud