Hyperparameter Tuning with scikit-learn
Hyperparameter tuning is a very important technique for improving the performance of deep learning models. In Chapter 4, Evaluating your Model with Cross Validation with Keras Wrappers, you learned about using a Keras wrapper with scikit-learn, which allows for Keras models to be used in a scikit-learn workflow. As a result, different general machine learning and data analysis tools and methods available in scikit-learn can be applied to Keras deep learning models. Among those methods are scikit-learn hyperparameter optimizers. In the previous chapter, you learned how to perform hyperparameter tuning by writing user-defined functions to loop over possible values for each hyperparameter. In this section, you will learn how to perform it in a much easier way by using various hyperparameter optimization methods available in scikit-learn. You will also get to practice applying those methods by completing an activity involving a real-life dataset.
