Linear Digressions

Machine learning: it can be fooled, just like you or me. Here's one of our favorite examples, a study into hacking neural networks. Relevant links: http://arxiv.org/pdf/1412.1897v4.pdf

5 Jan 201615min

Zipf's law is related to the statistics of how word usage is distributed. As it turns out, this is also strikingly reminiscent of how income is distributed, and populations of cities, and bug reports...

31 Dec 201511min

We've gone indie! Which shouldn't change anything about the podcast that you know and love, but we're super excited to keep bringing you Linear Digressions as a fully independent podcast. Some links...

30 Dec 20151min

It's Da Vinci meets Skynet: what makes a portrait beautiful, according to a machine learning algorithm. Snap a selfie and give us a listen.

27 Dec 201511min

Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!

18 Dec 201512min

Because there are more interesting problems than there are labeled datasets, semi-supervised learning provides a framework for getting feedback from the environment as a proxy for labels of what's "co...

4 Dec 20159min

Overfitting to your training data can be avoided by evaluating your machine learning algorithm on a holdout test dataset, but what about overfitting to the test data? Turns out it can be done, easily...

27 Nov 201515min

How many data scientists are there, where do they live, where do they work, what kind of tools do they use, and how do they describe themselves? RJMetrics wanted to know the answers to these question...

10 Nov 201515min