Linear Digressions

Latest Episodes:

t-SNE: Reduce Your Dimensions, Keep Your Clusters Jan 15, 2016

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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 the best tools on the market for doing dimensionality reduction when you have clustering in mind. Relevant links: https://www.youtube.com/watch?v=RJVL80Gg3lA

The [Expletive Deleted] Problem Jan 11, 2016

Show notes

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://en.wikipedia.org/wiki/Scunthorpe_problem https://www.washingtonpost.com/news/worldviews/wp/2016/01/05/where-is-russia-actually-mordor-in-the-world-of-google-translate/

Unlabeled Supervised Learning--whaaa? Jan 08, 2016

Show notes

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

Hacking Neural Nets Jan 05, 2016

Show notes

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

Zipf's Law Dec 31, 2015

Show notes

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 in software, as well as tons of other phenomena that we all interact with every day. Relevant links: http://economix.blogs.nytimes.com/2010/04/20/a-tale-of-many-cities/ http://arxiv.org/pdf/cond-mat/0412004.pdf https://terrytao.wordpress.com/2009/07/03/benfords-law-zipfs-law-and-the-pareto-distribution/

Indie Announcement Dec 30, 2015

Show notes

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 mentioned in the show: https://twitter.com/lindigressions https://twitter.com/benjaffe https://twitter.com/multiarmbandit https://soundcloud.com/linear-digressions http://lineardigressions.com/

Portrait Beauty Dec 27, 2015

Show notes

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.

The Cocktail Party Problem Dec 18, 2015

Show notes

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

A Criminally Short Introduction to Semi Supervised Learning Dec 04, 2015

Show notes

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 "correct." Of all the machine learning methodologies, it might also be the closest to how humans usually learn--we go through the world, getting (noisy) feedback on the choices we make and learn from the outcomes of our actions.

Thresholdout: Down with Overfitting Nov 27, 2015

Show notes

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, and you have to be very careful to avoid it. But an algorithm from the field of privacy research shows promise for keeping your test data safe from accidental overfitting

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