This chapter showed how to get started building and training neural networks to classify data including image recognition and physical activity data. One pitfall in machine learning is that more complex models will be more likely to overfit the training data, so that evaluating performance in the same data used to train the model results in biased, overly optimistic estimates of the model performance. Indeed, this can even make a difference as to which model is chosen as the best. Overfitting is also an issue for deep neural networks, and in the next chapter we will discuss various techniques used to prevent overfitting—termed regularization—and obtain more accurate estimates of model performance.