Complexity theory. Big-O. Constant, linear, logarithmic, and quadratic time versus space trade-offs. What does it actually mean when we say a function or an algorithm is efficient? How can we tell if we can do better? Join me, on this tour through a corner of computer science few developers actively think about, and you’ll walk away with a new way of looking at code and thinking about problems.


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Wade Wendorf at 11:46 on 10 Mar 2018

I enjoyed it, but didn't get as much out of it as I hoped. Good information tho and I took some useful bits away.

Anonymous at 11:49 on 10 Mar 2018

Lots of good information. Helped re-spark my own interest in a personal project.

Justin Foell at 12:00 on 10 Mar 2018

A great introduction into big-O notation and how to calculate and improve.

Good talk. I think I did walk away with a new way at looking at problems and code.

I thought this was a great overview of concepts I haven't thought about since school. Also learned a couple of 'tricks' that I can use to increase efficiency. Nice presentation.