In this chapter, we introduced deep neural networks as a way to generate models for complex data types where features are difficult to engineer. We examined how neural networks are trained through back-propagation, and why additional layers make this optimization intractable. We discussed solutions to this problem and demonstrated the use of the TensorFlow library to build an image classifier for hand-drawn digits.
Now that you have covered a wide range of predictive models, we will turn in the final two chapters to the last two tasks in generating analytical pipelines: turning the models that we have trained into a repeatable, automated process, and visualizing the results for ongoing insights and monitoring.
