In this chapter, we talked about using deep neural networks as binary classifiers. We spent quite a bit of time talking about network architecture design choices and touched on the idea that searching and experimentation is the best current way to choose an architecture.
We learned how to use the checkpoint callback in Keras to give us the power to go back in time and find a version of the model that has performance characteristics we like. Then we created and used a custom callback to measure ROC AUC score as the model trained. We wrapped up by looking at how to use the Keras .predict() method with traditional metrics from sklearn.metrics.
In the next chapter, we'll take a look at multiclass classification, and we will talk more ...
