In this chapter, the goal was to get you up and running in the exciting world of neural networks and deep learning. We examined how the methods work, their benefits, and their inherent drawbacks, with applications to two different datasets. These techniques work well where complex, nonlinear relationships exist in the data. The first example was of a simple neural network on a simple dataset. The second example showed the power of using Keras with TensorFlow backend on a challenging dataset, and the performance was exemplary. I hope you will apply these methods by themselves or supplement other methods in an ensemble modeling fashion. Good luck and good hunting! 

In the next chapter, we will learn about, ensembles, understand the data, and dive in deeper in modeling and evaluation.