In this chapter, we built a deep neural network in Keras and we found the optimal hyperparameters using the scikit-learn grid search. We also learned how to optimize a network by tuning the hyperparameters. Note that the results that we get might not be the same for all of us, but as long as we get similar predictions, we can consider our model a success. When you start training on new data, or if you're trying to address a different problem with a different dataset, you will have to go through this process again. In this chapter, we also learned about deep learning and hyperparameter optimization and explored how to apply them to the network to predict the onset of diabetes on a huge dataset of patients.
In the next chapter, we will look at how to classify DNA using machine algorithms.
