In this chapter, we'll discuss some of the intricacies of neural networks (NNs)—the cornerstone of deep learning (DL). We'll talk about their mathematical apparatus, structure, and training. Our main goal is to provide you with a systematic understanding of NNs. Often, we approach them from a computer science perspective—as a machine learning (ML) algorithm (or even a special entity) composed of a number of different steps/components. We gain our intuition by thinking in terms of neurons, layers, and so on (at least I did this when I first learned about this field). This is a perfectly valid way to do things and we can still do impressive things at this level of understanding. Perhaps this is not the correct approach, though.
NNs have solid mathematical foundations and if we approach them from this point of view, we...
