t-SNE: Reduce Your Dimensions, Keep Your Clusters
Ever tried to visualize a cluster of data points in 40 dimensions? Or even 4, for that matter? We prefer to stick to 2, or maybe 3 if we're feeling well-caffeinated. The t-SNE algorithm is one of t...
15 Tammi 201616min
The [Expletive Deleted] Problem
The town of [expletive deleted], England, is responsible for the clbuttic [expletive deleted] problem. This week on Linear Digressions: we try really hard not to swear too much. Related links: https...
11 Tammi 20169min
Unlabeled Supervised Learning--whaaa?
In order to do supervised learning, you need a labeled training dataset. Or do you...? Relevant links: http://www.cs.columbia.edu/~dplewis/candidacy/goldman00enhancing.pdf
8 Tammi 201612min
Hacking Neural Nets
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 Tammi 201615min
Zipf's Law
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 Joulu 201511min
Indie Announcement
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 Joulu 20151min
Portrait Beauty
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 Joulu 201511min
The Cocktail Party Problem
Grab a cocktail, put on your favorite karaoke track, and let’s talk some more about disentangling audio data!
18 Joulu 201512min
