Linear Digressions
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.
- Update frequency
- every 6 days
- Average duration
- 19 minutes
- Episodes
- 291
- Years Active
- 2014 - 2020
Machine Learning: The High Interest Credit Card of Technical Debt
This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the ide…
00:22:18 |
Mon 06 Nov 2017
Improving Upon a First-Draft Data Science Analysis
There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predic…
00:15:01 |
Mon 30 Oct 2017
Survey Raking
It's quite common for survey respondents not to be representative of the larger population from which they are drawn. But if you're a researcher, you need to study the larger population using data f…
00:17:23 |
Mon 23 Oct 2017
Happy Hacktoberfest
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 t…
00:15:40 |
Mon 16 Oct 2017
Re - Release: Kalman Runners
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 e…
00:17:53 |
Mon 09 Oct 2017
Neural Net Dropout
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, a…
00:18:53 |
Mon 02 Oct 2017
Disciplined Data Science
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 bein…
00:29:34 |
Mon 25 Sep 2017
Hurricane Forecasting
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 i…
00:27:57 |
Mon 18 Sep 2017
Finding Spy Planes with Machine Learning
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 fi…
00:18:09 |
Mon 11 Sep 2017
Data Provenance
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, thoug…
00:22:48 |
Mon 04 Sep 2017
Adversarial Examples
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…
00:16:11 |
Mon 28 Aug 2017
Jupyter Notebooks
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, do…
00:15:50 |
Mon 21 Aug 2017
Curing Cancer with Machine Learning is Super Hard
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 …
00:19:20 |
Mon 14 Aug 2017
KL Divergence
Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution. It comes to us originally from information the…
00:25:38 |
Mon 07 Aug 2017
Sabermetrics
It's moneyball time! SABR (the Society for American Baseball Research) is the world's largest organization of statistics-minded baseball enthusiasts, who are constantly applying the craft of scienti…
00:25:48 |
Mon 31 Jul 2017
What Data Scientists Can Learn from Software Engineers
We're back again with friend of the pod Walt, former software engineer extraordinaire and current data scientist extraordinaire, to talk about some best practices from software engineering that are r…
00:23:46 |
Mon 24 Jul 2017
Software Engineering to Data Science
Data scientists and software engineers often work side by side, building out and scaling technical products and services that are data-heavy but also require a lot of software engineering to build an…
00:19:05 |
Mon 17 Jul 2017
Re-Release: Fighting Cholera with Data, 1854
This episode was first released in November 2014.
In the 1850s, there were a lot of things we didn’t know yet: how to create an airplane, how to split an atom, or how to control the spread of a comm…
00:12:04 |
Mon 10 Jul 2017
Re-Release: Data Mining Enron
This episode was first release in February 2015.
In 2000, Enron was one of the largest and companies in the world, praised far and wide for its innovations in energy distribution and many other mark…
00:32:16 |
Sun 02 Jul 2017
Factorization Machines
What do you get when you cross a support vector machine with matrix factorization? You get a factorization machine, and a darn fine algorithm for recommendation engines.
00:19:54 |
Mon 26 Jun 2017
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