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
A Criminally Short Introduction to Semi Supervised Learning
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 Joulu 20159min
Thresholdout: Down with Overfitting
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 Marras 201515min
The State of Data Science
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 Marras 201515min
