In this chapter, we first learned about a simple FNN, known as the MLP, and broke it down into its individual components to get a deeper understanding of how they work and are constructed. We then extended these concepts to further our understanding of deep neural networks. You should now have intimate knowledge of how FNNs work and understand how various models are constructed, as well as understand how to build and possibly improve them for yourself.
Let's now move on to the next chapter, where we will learn how to improve our neural networks so that they generalize better on unseen data.
