Introduction

In preceding chapters, we've spent time assessing powerful techniques that enable the analysis of complex or challenging data. However, for the most difficult problems, the right technique will only get you so far.

The persistent challenge that deep learning and supervised learning try to solve for is that finding solutions often requires multiple big investments from the team in question. Under the old paradigm, one often has to perform specific preparation tasks, requiring time, specialist skills, and knowledge. Often, even the techniques used were domain-specific and/or data type-specific. This process, via which features are derived, is referred to as feature engineering.

Most of the deep learning algorithms which we've studied so far are intended to help find ways around needing to perform extensive feature engineering. However, at the same time, feature engineering continues to be seen as a hugely important skill for top-level ML practitioners. The following...