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
