Summary

We covered a lot of ground in this chapter. We looked at activation functions and built our first true deep learning models using MXNet. Then we took a real-life dataset and created two use cases for applying a machine learning model. The first use case was to predict which customers will return in the future based on their past activity. This was a binary classification task. The second use case was to predict how much a customer will spend in the future based on their past activity. This was a regression task. We ran both models first on a small dataset and used different machine learning libraries to compare them against our deep learning model. Our deep learning model out-performed all of the algorithms.

We then took this further by using a dataset that was 100 times bigger. We built a larger deep learning model and adjusted our parameters to get an increase in our binary classification task accuracy. We finished the chapter with a brief discussion on how deep learning models out-perform traditional machine learning algorithms on large datasets.

In the next chapter, we will look at computer vision tasks, which deep learning has revolutionized.