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
Agile Development for Data Scientists, Part 1: The Good
If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work…
00:25:56 |
Sun 19 Aug 2018
Re - Release: How To Lose At Kaggle
We've got a classic for you this week as we take a week off for the dog days of summer. See you again next week!
Competing in a machine learning competition on Kaggle is a kind of rite of passage fo…
00:17:54 |
Mon 13 Aug 2018
Troubling Trends In Machine Learning Scholarship
There's a lot of great machine learning papers coming out every day--and, if we're being honest, some papers that are not as great as we'd wish. In some ways this is symptomatic of a field that's gro…
00:29:35 |
Mon 06 Aug 2018
Can Fancy Running Shoes Cause You To Run Faster?
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 article from the NY Times about a new running shoe that Nike claims can make …
00:28:37 |
Sun 29 Jul 2018
Compliance Bias
When you're using an AB test to understand the effect of a treatment, there are a lot of assumptions about how the treatment (and control, for that matter) get applied. For example, it's easy to thin…
00:23:28 |
Sun 22 Jul 2018
AI Winter
Artificial Intelligence has been widely lauded as a solution to almost any problem. But as we justapose the hype in the field against the real-world benefits we see, it raises the question: Are we co…
00:19:02 |
Sun 15 Jul 2018
Rerelease: How to Find New Things to Learn
We like learning on vacation. And we're on vacation, so we thought we'd re-air this episode about how to learn.
Original Episode: https://lineardigressions.com/episodes/2017/5/14/how-to-find-new-thi…
00:18:32 |
Sun 08 Jul 2018
Rerelease: Space Codes
We're on vacation on Mars, so we won't be communicating with you all directly this week. Though, if we wanted to, we could probably use this episode to help get started.
Original Episode: http://lin…
00:24:30 |
Mon 02 Jul 2018
Rerelease: Anscombe's Quartet
We're on vacation, so we hope you enjoy this episode while we each sip cocktails on the beach.
Original Episode: http://lineardigressions.com/episodes/2017/6/18/anscombes-quartet
Original Summary: …
00:16:14 |
Mon 25 Jun 2018
Rerelease: Hurricanes Produced
Now that hurricane season is upon us again (and we are on vacation), we thought a look back on our hurricane forecasting episode was prudent. Stay safe out there.
00:28:12 |
Mon 18 Jun 2018
GDPR
By now, you have probably heard of GDPR, the EU's new data privacy law. It's the reason you've been getting so many emails about everyone's updated privacy policy.
In this episode, we talk about som…
00:18:24 |
Mon 11 Jun 2018
Git for Data Scientists
If you're a data scientist, chances are good that you've heard of git, which is a system for version controlling code. Chances are also good that you're not quite as up on git as you want to be--git …
00:22:05 |
Sun 03 Jun 2018
Analytics Maturity
Data science and analytics are hot topics in business these days, but for a lot of folks looking to bring data into their organization, it can be hard to know where to start and what it looks like wh…
00:19:32 |
Sun 20 May 2018
SHAP: Shapley Values in Machine Learning
Shapley values in machine learning are an interesting and useful enough innovation that we figured hey, why not do a two-parter? Our last episode focused on explaining what Shapley values are: they d…
00:19:12 |
Sun 13 May 2018
Game Theory for Model Interpretability: Shapley Values
As machine learning models get into the hands of more and more users, there's an increasing expectation that black box isn't good enough: users want to understand why the model made a given predictio…
00:27:06 |
Mon 07 May 2018
AutoML
If you were a machine learning researcher or data scientist ten years ago, you might have spent a lot of time implementing individual algorithms like decision trees and neural networks by hand. If yo…
00:15:24 |
Mon 30 Apr 2018
CPUs, GPUs, TPUs: Hardware for Deep Learning
A huge part of the ascent of deep learning in the last few years is related to advances in computer hardware that makes it possible to do the computational heavy lifting required to build models with…
00:12:40 |
Mon 23 Apr 2018
A Technical Introduction to Capsule Networks
Last episode we talked conceptually about capsule networks, the latest and greatest computer vision innovation to come out of Geoff Hinton's lab. This week we're getting a little more into the techni…
00:31:28 |
Mon 16 Apr 2018
A Conceptual Introduction to Capsule Networks
Convolutional nets are great for image classification... if this were 2016. But it's 2018 and Canada's greatest neural networker Geoff Hinton has some new ideas, namely capsule networks. Capsule nets…
00:14:05 |
Mon 09 Apr 2018
Convolutional Neural Nets
If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details…
00:21:55 |
Mon 02 Apr 2018
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