Machine Learning for Finance

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 ...