In this chapter, we will use Keras and TensorFlow to solve a tricky binary classification problem. We will start by talking about the benefits and drawbacks of deep learning for this type of problem, and then we will go right into developing a solution using the same framework we established in Chapter 2, Using Deep Learning to Solve Regression Problems. Finally, we will cover Keras callbacks in greater depth and even use a custom callback to implement a per epoch receiver operating characteristic / area under the curve (ROC AUC) metric.

We will cover the following topics in this chapter:

• Binary classification and deep neural networks
• Case study – epileptic seizure recognition
• Building a binary classifier in Keras
• Using the checkpoint callback in Keras
• Measuring ROC AUC in a custom callback
• Measuring precision, recall, and f1-score