Now comes the most exciting part and probably the hottest technical term of this century. Reality apart, we now understand the learning to some extent, so let's get to the first part of the term deep learning—deep.
DL is a type of machine learning but it is purely based on neural networks. We will take a look at neural networks too but in the next chapter. The basic objective of any machine learning system is to learn useful representations of the data given to it. But what makes DL different? It turns out that DL systems treat data as a representation of layers. For example, an image can be treated as a representation of layers of varying properties such as edges, contours, orientation, texture, and gradients. The following diagram from the book, Deep Learning with Python, by François Chollet captures this idea nicely:
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