TensorFlow as a Feature Engineering DSL

Pablo Duboue

Keywords: feature engineering, dsl, tensorflow, keras, target-rate encoding, one-hot encoding

TL;DR: Frameworks for implementing Deep Learning networks can also be used as frameworks for feature engineering.

Abstract:

Deep Neural Networks have fulfilled the promise to reduce the need of
feature engineering by leveraging deep networks and training data. An
interesting side effect, maybe unexpected, is that frameworks for
implementing Deep Learning networks can also be used as frameworks for
feature engineering. We sketch here some ideas for a DSL implemented
on top of TensorFlow, hoping to attract interest from both the deep
learning computational graph and programming languages communities.