As some of you may know, one of my side interests is approximate nearest neighbor algorithms. I'm the author of Annoy, a library with 3,500+ stars on Github as of today. It offers fast approximate search for nearest neighbors with the additional benefit that you can load data super fast from disk using mmap.
UPDATE(2018-06-17): There are is a later blog post with newer benchmarks!
One of my super nerdy interests include approximate algorithms for nearest neighbors in high-dimensional spaces. The problem is simple. You have say 1M points in some high-dimensional space.
I've been a bit bad at posting things with a regular cadence lately, partly because I'm trying to adjust to having a toddler, partly because the hunt for clicks has caused such a high bar for me that I feel like I have to post something Pulitzer-worthy.
As you may know, one of my (very geeky) interests is Approximate nearest neigbor methods, and I'm the author of a Python package called Annoy.
I've also built a benchmark suite called ann-benchmarks to compare different packages.
This is another post based on my talk at NYC Machine Learning. The previous two parts covered most of the interesting parts, but there are still some topics left to be discussed. To go back and read the meaty stuff, check out
This is a blog post rewritten from a presentation at NYC Machine Learning on Sep 17. It covers a library called Annoy that I have built that helps you do nearest neighbor queries in high dimensional spaces.
This is a blog post rewritten from a presentation at NYC Machine Learning last week. It covers a library called Annoy that I have built that helps you do (approximate) nearest neighbor queries in high dimensional spaces.
Annoy is a library written by me that supports fast approximate nearest neighbor queries. Say you have a high (1-1000) dimensional space with points in it, and you want to find the nearest neighbors to some point.
Sometimes you have these awesome insights. A few days ago I got an idea for how to improve index building in Annoy.
For anyone who isn't acquainted with Annoy – it's a C++ library with Python bindings that provides fast high-dimensional nearest neighbor search.
Annoy is a C++/Python package I built for fast approximate nearest neighbor search in high dimensional spaces. Spotify uses it a lot to find similar items. First, matrix factorization gives a low dimensional representation of each item (artist/album/track/user) so that every item is a k-dimensional vector, where k is typically 40-100.