Linear Digressions

Latest Episodes:

Happy Hacktoberfest Oct 16, 2017

Show notes

It's the middle of October, so you've already made two pull requests to open source repos, right? If you have no idea what we're talking about, spend the next 20 minutes or so with us talking about the importance of open source software and how you can get involved. You can even get a free t-shirt! Hacktoberfest main page: https://hacktoberfest.digitalocean.com/#details

Re - Release: Kalman Runners Oct 09, 2017

Show notes

In honor of the Chicago marathon this weekend (and due in large part to Katie recovering from running in it...) we have a re-release of an episode about Kalman filters, which is part algorithm part elaborate metaphor for figuring out, if you're running a race but don't have a watch, how fast you're going. Katie's Chicago race report: miles 1-13: light ankle pain, lovely cool weather, the most fun EVAR miles 13-17: no more ankle pain but quads start getting tight, it's a little more effort miles 17-20: oof, really tight legs but still plenty of gas in then tank. miles 20-23: it's warmer out now, legs hurt a lot but running through Pilsen and Chinatown is too fun to notice mile 24: ugh cramp everything hurts miles 25-26.2: awesome crowd support, really tired and loving every second Final time: 3:54:35

Neural Net Dropout Oct 02, 2017

Show notes

Neural networks are complex models with many parameters and can be prone to overfitting. There's a surprisingly simple way to guard against this: randomly destroy connections between hidden units, also known as dropout. It seems counterintuitive that undermining the structural integrity of the neural net makes it robust against overfitting, but in the world of neural nets, weirdness is just how things go sometimes. Relevant links: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf

Disciplined Data Science Sep 25, 2017

Show notes

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level. Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices. We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Hurricane Forecasting Sep 18, 2017

Show notes

It's been a busy hurricane season in the Southeastern United States, with millions of people making life-or-death decisions based on the forecasts around where the hurricanes will hit and with what intensity. In this episode we'll deconstruct those models, talking about the different types of models, the theory behind them, and how they've evolved through the years.

Finding Spy Planes with Machine Learning Sep 11, 2017

Show notes

There are law enforcement surveillance aircraft circling over the United States every day, and in this episode, we'll talk about how some folks at BuzzFeed used public data and machine learning to find them. The fun thing here, in our opinion, is the blend of intrigue (spy planes!) with tech journalism and a heavy dash of publicly available and reproducible analysis code so that you (yes, you!) can see exactly how BuzzFeed identifies the surveillance planes.

Data Provenance Sep 04, 2017

Show notes

Software engineers are familiar with the idea of versioning code, so you can go back later and revive a past state of the system. For data scientists who might want to reconstruct past models, though, it's not just about keeping the modeling code. It's also about saving a version of the data that made the model. There are a lot of other benefits to keeping track of datasets, so in this episode we'll talk about data lineage or data provenance.

Adversarial Examples Aug 28, 2017

Show notes

Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes. Today we have a roundup of a few successful efforts to create robust adversarial examples, including what it means for an adversarial example to be robust and what this might mean for machine learning in the future.

Jupyter Notebooks Aug 21, 2017

Show notes

This week's episode is just in time for JupyterCon in NYC, August 22-25... Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doing quick visualizations, and packaging code snippets with explanations for sharing your work with others. If you're not a data person, or you are but you haven't tried out Jupyter notebooks yet, here's your nudge to go give them a try. In this episode we'll go back to the old days, before notebooks, and talk about all the ways that data scientists like to work that wasn't particularly well-suited to the command line + text editor setup, and talk about how notebooks have evolved over their lifetime to become even more powerful and well-suited to the data scientist's workflow.

Curing Cancer with Machine Learning is Super Hard Aug 14, 2017

Show notes

Today, a dispatch on what can go wrong when machine learning hype outpaces reality: a high-profile partnership between IBM Watson and MD Anderson Cancer Center has recently hit the rocks as it turns out to be tougher than expected to cure cancer with artificial intelligence. There are enough conflicting accounts in the media to make it tough to say exactly went wrong, but it's a good chance to remind ourselves that even in a post-AI world, hard problems remain hard.

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