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
The Care and Feeding of Data Scientists: Becoming a Data Science Manager
Data science management isn’t easy, and many data scientists are finding themselves learning on the job how to manage data science teams as they get promoted into more formal leadership roles. O’Reil…
00:24:45 |
Mon 28 Oct 2019
Procella: YouTube's super-system for analytics data storage
If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs a…
00:29:48 |
Mon 21 Oct 2019
Kalman Runners
The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation. If yo…
00:15:59 |
Sun 13 Oct 2019
What's *really* so hard about feature engineering?
Feature engineering is ubiquitous but gets surprisingly difficult surprisingly fast. What could be so complicated about just keeping track of what data you have, and how you made it? A lot, as it tur…
00:21:18 |
Sun 06 Oct 2019
Data storage for analytics: stars and snowflakes
If you’re a data scientist or data engineer thinking about how to store data for analytics uses, one of the early choices you’ll have to make (or live with, if someone else made it) is how to lay out…
00:15:22 |
Mon 30 Sep 2019
Data storage: transactions vs. analytics
Data scientists and software engineers both work with databases, but they use them for different purposes. So if you’re a data scientist thinking about the best way to store and access data for your …
00:16:08 |
Mon 23 Sep 2019
GROVER: an algorithm for making, and detecting, fake news
There are a few things that seem to be very popular in discussions of machine learning algorithms these days. First is the role that algorithms play now, or might play in the future, when it comes to…
00:18:28 |
Mon 16 Sep 2019
Data science teams as innovation initiatives
When a big, established company is thinking about their data science strategy, chances are good that whatever they come up with, it’ll be somewhat at odds with the company’s current structure and pro…
00:15:21 |
Mon 09 Sep 2019
Can Fancy Running Shoes Cause You To Run Faster?
This is a re-release of an episode that originally aired on July 29, 2018.
The stars aligned for me (Katie) this past weekend: I raced my first half-marathon in a long time and got to read a great a…
00:30:15 |
Sun 01 Sep 2019
Organizational Models for Data Scientists
When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t po…
00:23:09 |
Sun 25 Aug 2019
Data Shapley
We talk often about which features in a dataset are most important, but recently a new paper has started making the rounds that turns the idea of importance on its head: Data Shapley is an algorithm …
00:16:55 |
Mon 19 Aug 2019
A Technical Deep Dive on Stanley, the First Self-Driving Car
This is a re-release of an episode that first ran on April 9, 2017.
In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this we…
00:41:32 |
Mon 12 Aug 2019
An Introduction to Stanley, the First Self-Driving Car
In October 2005, 23 cars lined up in the desert for a 140 mile race. Not one of those cars had a driver. This was the DARPA grand challenge to see if anyone could build an autonomous vehicle capabl…
00:14:19 |
Mon 05 Aug 2019
Putting the "science" in data science: the scientific method, the null hypothesis, and p-hacking
The modern scientific method is one of the greatest (perhaps the greatest?) system we have for discovering knowledge about the world. It’s no surprise then that many data scientists have found their …
00:24:11 |
Mon 29 Jul 2019
Interleaving
If you’re Google or Netflix, and you have a recommendation or search system as part of your bread and butter, what’s the best way to test improvements to your algorithm? A/B testing is the canonical …
00:16:54 |
Mon 22 Jul 2019
Federated Learning
This is a re-release of an episode first released in May 2017.
As machine learning makes its way into more and more mobile devices, an interesting question presents itself: how can we have an algori…
00:15:03 |
Sun 14 Jul 2019
Endogenous Variables and Measuring Protest Effectiveness
This is a re-release of an episode first released in February 2017.
Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers? It's a …
00:17:58 |
Sun 07 Jul 2019
Deepfakes
Generative adversarial networks (GANs) are producing some of the most realistic artificial videos we’ve ever seen. These videos are usually called “deepfakes”. Even to an experienced eye, it can be a…
00:15:08 |
Mon 01 Jul 2019
Revisiting Biased Word Embeddings
The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias arou…
00:18:09 |
Mon 24 Jun 2019
Attention in Neural Nets
There’s been a lot of interest lately in the attention mechanism in neural nets—it’s got a colloquial name (who’s not familiar with the idea of “attention”?) but it’s more like a technical trick that…
00:26:32 |
Mon 17 Jun 2019
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