Before writing this section, I was thinking about the many ways we can draw a line between machine learning and deep learning. Each of them was contradictory in some way. In truth, you can't separate deep learning from machine learning because deep learning is a subfield of machine learning. Deep learning studies a specific set of models called neural networks. The first mentions of the mathematical foundations of neural networks date back to the 1980s, and the theory behind modern neural networks originated in 1958. Still, they failed to show good results until the 2010s. Why?
The answer is simple: hardware. Training big neural networks uses a great amount of computation power. But not any computation power will suffice. It turns out that neural networks do a lot of matrix operations under the hood. Strangely, rendering computer graphics also...