Following topics, according to me, are also relevant when it comes to deeper understanding of Extracting Features with Transformers
We covered removing noise to improve features; however, improved performance can be obtained for some datasets by adding noise. The reason for this is simple—it helps stop overfitting by forcing the classifier to generalize its rules a little (although too much noise will make the model too general). Try implementing a Transformer that can add a given amount of noise to a dataset. Test that out on some of the datasets from UCI ML and see if it improves test-set performance.
Vowpal Wabbit is a great project, providing very fast feature extraction for text-based problems. It comes with a Python wrapper, allowing you to call it from with Python code. Test it out on large datasets.