A Technical Deep Dive on Stanley, the First Self-Driving Car
In our follow-up episode to last week's introduction to the first self-driving car, we will be doing a technical deep dive this week and talking about the most important systems for getting a car to d...
10 Huhti 201740min
An Introduction to Stanley, the First Self-Driving Car
In October 2005, 23 cars lined up in the desert for a 140 mile race. Not one of those cars had a driver. This was the DARPA grand challenge to see if anyone could build an autonomous vehicle capable...
3 Huhti 201713min
Feature Importance
Figuring out what features actually matter in a model is harder to figure out than you might first guess. When a human makes a decision, you can just ask them--why did you do that? But with machine ...
27 Maalis 201720min
Space Codes!
It's hard to get information to and from Mars. Mars is very far away, and expensive to get to, and the bandwidth for passing messages with Earth is not huge. The messages you do pass have to travers...
20 Maalis 201723min
Finding (and Studying) Wikipedia Trolls
You may be shocked to hear this, but sometimes, people on the internet can be mean. For some of us this is just a minor annoyance, but if you're a maintainer or contributor of a large project like Wi...
13 Maalis 201715min
A Sprint Through What's New in Neural Networks
Advances in neural networks are moving fast enough that, even though it seems like we talk about them all the time around here, it also always seems like we're barely keeping up. So this week we have...
6 Maalis 201716min
Stein's Paradox
When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you ...
27 Helmi 201727min
Empirical Bayes
Say you're looking to use some Bayesian methods to estimate parameters of a system. You've got the normalization figured out, and the likelihood, but the prior... what should you use for a prior? Em...
20 Helmi 201718min
