Summary

And that's it! We've learned how neural networks work. Throughout the rest of this book, we'll look at how to build more complex neural networks that can approximate more complex functions.

As it turns out, there are a few tweaks to make to the basic structure for it to work well on specific tasks, such as image recognition. The basic ideas introduced in this chapter, however, stay the same:

• Neural networks function as approximators

• We gauge how well our approximated function, , performs through a loss function

• Parameters of the model are optimized by updating them in the opposite direction of the derivative of the loss function with respect to the parameter

• The derivatives are calculated backward through the model using the chain rule in a process called backpropagation

The key takeaway from this chapter is that while we are looking for function f, we can try and find it by optimizing a function to perform like f on a dataset. A subtle but important distinction is that we do not know whether works like f at all. An often-cited example is a military project that tried to use deep learning to spot tanks within images. The model trained well on the dataset, but once the Pentagon wanted to try out their new tank spotting device, it failed miserably.

In the tank example, it took the Pentagon a while to figure out that in the dataset they used to develop the model, all the pictures of the tanks were taken on a cloudy day and pictures without a tank where taken on a sunny day. Instead of learning to spot tanks, the model had learned to spot grey skies instead.

This is just one example of how your model might work very differently to how you think, or even plan for it to do. Flawed data might seriously throw your model off track, sometimes without you even noticing. However, for every failure, there are plenty of success stories in deep learning. It is one of the high-impact technologies that will reshape the face of finance.

In the next chapter, we will get our hands dirty by jumping in and working with a common type of data in finance, structured tabular data. More specifically, we will tackle the problem of fraud, a problem that many financial institutions sadly have to deal with and for which modern machine learning is a handy tool. We will learn about preparing data and making predictions using Keras, scikit-learn, and XGBoost.