In this chapter, we learned about some important mathematical topics, such as the difference between Euclidean and non-Euclidean data and manifolds. We then went on to learn about a few fascinating and emerging topics in the field of deep learning that have widespread applications in a plethora of domains in which traditional deep learning algorithms have proved to be ineffective. This new class of neural networks, known as graph neural networks, greatly expand on the usefulness of deep learning by extending it to work on non-Euclidean data. Toward the end of this chapter, we saw an example use case for graph neural networks—facial recognition in 3D.
This brings us to the end of this book. Congratulations on successfully completing the lessons that were provided!
