Extracting Features with Transformers

Following topics, according to me, are also relevant when it comes to deeper understanding of Extracting Features with Transformers

Adding noise

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

URL: http://hunch.net/~vw/

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.

word2vec

URL: https://radimrehurek.com/gensim/models/word2vec.html

Word embeddings are receiving a...