Python

A command line utility to create kernels in Jupyter from conda and virtual environments. Available on github and pypi.

I am a contributor to PyMC3, a “Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.”

Both a precious pup and a task runner for Python. Available on github and pypi.

A demonstration of using Flask, React, and d3js to visualize machine learning models. This is a port of a previous project from Angular to React.

Using hundreds of thousands of historical cross country running results to make predictions about future meets. The page is updated more than weekly during the season.

For the second year in a row, I took part in Kaggle’s contest to predict March Madness winners. The code for the actual model is not very expository (get in touch if you are interested), but I also built a friendlier page to query predictions interactively at the link.

A command line utility for automatically compiling $\LaTeX$, and eliminating auxiliary files. Try it out with pip install tidytex.

An essay on building linear regression models. It is converted from notes for a talk I gave at Rice University in September 2014. Contains lots of pictures, lots of interactivity, and a modest amount of math. As a bonus, everything is typeset with KaTeX.

A demonstration of linear regression, overfitting, normalization, and regularization. Allows you to choose data from a distribution, and interactively fit polynomials to the data using least squares, ridge, or Lasso regression.

A demonstration of using Flask, AngularJS, and d3js to visualize machine learning models. This is meant to be a minimal example of how to put together such a demo, showing how to make the tech stack play nice.