abeinstein

How do you automate these?

My favorite explanation of Kalman filters is this tutorial in Python: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

It's geared towards software engineers and doesn't assume much math background. Highly recommend.

Uber - Trust & Safety | San Francisco | Full-time | ONSITE

Uber's Trust & Safety team is hiring backend, mobile, and full-stack engineers. We develop technology to help make millions of rides safer every day.

Our tech stack includes Go, Java, Kafka/Samza, Spark, Python, Node.js, Cassandra. We work on a bunch of interesting problems in signal processing, machine learning, and distributed systems.

Some recent launches:

http://www.recode.net/2016/6/29/12053194/uber-madd-driver-sa...

http://venturebeat.com/2016/09/23/uber-selfies-security-phot...

Please contact me at abeinstein AT uber DOT com if interested.

Source: http://www.brendangregg.com/linuxperf.html

The 'Get the Code' link is broken (https://chrome.com/cubelab#create). Does anyone have a link to the code?

This may be helpful for people like me who don't know anything about health insurance: https://www.healthsherpa.com/learn/how-insurance-works

Hm, it's very possible that SAT prep companies game the system by making their diagnostic tests quite difficult.

Yeah I meant more than it is very easy to go from the 400-500's range to the 600-700's range with SAT prep. Especially on the writing section -- they only test on 20ish grammar rules, many of which are subtle and wouldn't necessarily be noticed without prep.

As a former SAT prep teacher, I'm very excited to learn about Khan Academy's partnership with the College Board. Previously, success on the SAT seemed to depend solely on how much money was spent on SAT prep. Glad to hear that the College Board is interested in leveling the playing field.

So, NSA has solved P vs NP and they're just not telling us?

I have a hunch that it's going to be about getting useful and relevant information out of big data. Analyzing 'Big Data' using techniques from machine learning is getting a lot of hype, as it is starting to prove itself as a powerful predictive tool. Companies that will be big in the future will take advantage of these techniques to solve major problems in education, healthcare, robotics, etc.