Hi, I'm Chris. I'm a data scientist, hacker and person who cares. I like building data products and making technology work for people, not vice versa. I was a particle physicist once. I climb small walls and play loud guitars. I don't tweet much.
Fast Forward Labs posts
Fizzing the proverbial buzz.
A custom scikit-learn classifier implementing the NBSVM model described in Baselines and Bigrams: Simple, Good Sentiment and Topic Classification and used as the baseline for the Fast Forward Labs report on transfer learning for NLP.
Observable notebook in which I construct a demonstration of active learning with logistic regression live in the browser with TensorFlow.js. Required some fiddling with the reactive cell evaluation of Observable.
Observable notebook in which a recurrent neural net trained on the corpus of Shakespeare plays completes your sentence, using Keras.js. The accompanying GitHub repo contains the data and notebook needed to train. If I were to re-do this project, I would write the training procedure into a library, not a notebook.
A minimal viable text classification app built around a fictional business problem. I use this for teaching and demonstrating how I architect small machine learning projects, following a Functional Core, Imperative Shell pattern: creating a library of reusable, functional components, and keeping stateful operations to short scripts.
The tiniest Flask app, because I wanted to play with deploying to Heroku.
A very tiny project documenting the result of the longer-than-it-should-have-taken to set up a working PySpark testing environment, creating a new Spark context only once per full test run.