Big O
A visual introduction to big O notation.
16 posts
A visual introduction to big O notation.
Describes a simple Markov chain algorithm to generate reasonable-sounding but utterly nonsensical text, and presents some example outputs as well as a Python implementation.
I use this website as a memory dump for gamedev things i might need in the future. I will update it with new things when i feel like it or when i need to forget things to make room for more. If you have any questions ask on twitter.
Note: This is for my own personal memory dump so it's written to
Hi there! This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science. When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn't be stumped when asked about them. Over the last few years, I've interviewed at several Silicon Valley startups, and also some bigger companies, like Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to msyelf "Why oh why hasn't someone created a nice Big-O cheat sheet?". So, to save all of you fine folks a ton of time, I went ahead and created one. Enjoy!
IDEA is a series of nonverbal algorithm assembly instructions, created by Sándor P. Fekete and Sebastian Morr.
A really clear visual explanation of what a Markova chain is and what it is good for.
Kalman filters are a mathematical process for smoothing out a noisy signal (its more complicated than that) its a central algorithm in robotics and real time sensing systems in general.
I’m trying to get smart about Kalman filters. This is a great resource
Posted in r/compsci by u/adriansky • 125 points and 3 comments
There are lots of algorithm libraries. This one is a nice one!
Handy summary
Major resource page for the Kalman Filter
More about the famous Kalman Filter
More Kalman Filter links. This one is one of the best.
Ive been doing a deep dive on Kalman Filters. Here are one of the best explanations. Beware it's hard.