K-Fold cross-validation

If you're experienced with machine learning, you may be wondering why I would opt for Hold-Out (train/val/test) validation over K-Fold cross-validation. Training a deep neural network is a very expensive operation, and put very simply, training K of them per set of hyperparameters we'd like to explore is usually not very practical.

We can be somewhat confident that Hold-Out validation does a very good job, given a large enough val and test set. Most of the time, we are hopefully applying deep learning in situations where we have an abundance of data, resulting in an adequate val and test set.

Ultimately, it's up to you. As we will see later, Keras provides a scikit-learn interface that allows Keras models to be integrated into a scikit-learn pipeline. This allows us to perform K-Fold, Stratified K-Fold, and even grid searches with K-Fold. It's both possible and appropriate to sometimes use K-Fold CV in training deep models. That said, for the rest of the book we will focus on the using Hold-Out validation.