This is the site for Scott Sievert, a graduate student at UW–Madison. I am a mathematician-in-training, think Python is pretty sweet 🐍 and love to ski 🎿!

Favorite posts

I mark my favorite posts with and aggregate them at Favorite Posts

Blog

I tend to blog about technical topics, including interesting mathematics and cool applications of Python. A complete lists of my posts can be found at Blog.

• Launching tasks from workers with Dask

    Jan 4, 2018 in systems distributed dask

  Dask recently surprised me with it’s flexibility in a recent use case, even more than the basic use detailed in a previous post. I’ll walk through my use case and the interesting problem it highlights. I’ll show a toy solution and point to the relevant parts of the Dask documentation.

  Read on →
  



• PyTorch: fast and simple

    Sep 7, 2017 in optimization computing speed framework

  I recently came across PyTorch, a new technology prime for optimization and machine learning. The docs make it look attractive, so immediately I wondered “how does it compare with NumPy?”

  Turns out it’s a pretty nice framework that’s fast and straightforward to use. I’ll detail the speed before talking about ease-of-use.

  Read on →
  



• Holoviews interactive visualization

    Jul 22, 2017 in data-visualization web

  I often want to provide some simple interactive visualizations for this blog. I like to include visualization to give some sense of how the data change as various parameters are changed. Examples can be found in Finding sparse solutions to linear systems, Least squares and regularization, and Computer color is only kinda broken.

  I have discovered a new tool, Holoviews to create these widgets. I want to create these interactive widgets for my blog, meaning I want to embed these in a static HTML page. Previously, I used Jake Vanderplas’s ipywidgets-static but in this post I’ll walk through creating a widget.

  Read on →
  



• Apple CoreML model conversion

    Jun 11, 2017 in apple ios machine-learning

  Apple has created a new file format for machine learning models. These files can be used easily to predict, regardless of the creation process, which means that “Apple Introduces Core ML” draws an analogy between these files and PDFs. It’s possible to generate predictions with only this file, and none of the creation libraries.

  Generating predictions is a pain point faced by data scientists today and often involves the underlying math. At best, this involves using training the model in Python and then calling the underlying C library in the production app.

  This file format will only become widely used if easy conversion from popular machine learning libraries is possible and predictions are simple to generate. Apple made these claims during their WWDC 2017 keynote. I want to investigate their claim.

  

  Read on →
  

All posts →