Chapter 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features
Thus far, we have studied predictive modeling techniques that use a set of features (columns in a tabular dataset) that are pre-defined for the problem at hand. For example, a user account, an internet transaction, a product, or any other item that is important to a business scenario are often described using properties derived from domain knowledge of a particular industry. More complex data, such as a document, can still be transformed into a vector representing something about the words in the text, and images can be represented by matrix factors as we saw in Chapter 6, Words and Pixels – Working with Unstructured Data. However, with both simple and complex data types, we could easily imagine higher-level interactions between features (for example, a user in a certain country and age range using a particular device is more likely to click on a webpage, while none of these three factors alone are predictive...
