Feature engineering is the process of creating new features by transforming existing ones. It is very important in traditional ML but is less important in deep learning.
Traditionally, the data scientists or the researchers would apply their domain knowledge and come up with a smart representation of the input that would highlight the relevant feature and make the prediction task more accurate.
For example, before the advent of deep learning, traditional computer vision required custom algorithms that were extracting the most relevant features, such as edge detection or Scale-Invariant Feature Transform (SIFT).
To understand this concept, let's look at an example. Here, we see an original photo:
And, after some feature engineering—in particular, after running an edge detection algorithm, we get the following result:
One of the great advantages...
