In this chapter, we added a whole bunch of skills to our list as a machine learning practitioner. Not only did we cover the basics of artificial neural networks, including perceptrons and MLPs, we also got our hands on some advanced deep learning software. We learned how to build a simple perceptron from scratch and how to build state-of-the-art networks using Keras. Furthermore, we learned about all the details of neural nets: activation functions, loss functions, layer types, and training methods. All in all, this was probably the most intensive chapter yet.
Now that you know about most of the essential supervised learners, it is time to talk about how to combine different algorithms into a more powerful one. Thus, in the next chapter, we will talk about how to build ensemble classifiers.
