In part 2 of the word2vec tutorial (here’s part 1), I’ll cover a few additional modifications to the basic skip-gram model which are important for actually making it feasible to train.
DBSCAN is a popular clustering algorithm which is fundamentally very different from k-means.
In this post I’m sharing a technique I’ve found for showing which words in a piece of text contribute most to its similarity with another piece of text when using Latent Semantic Indexing (LSI) to represent the two documents. This has proven valuable to me in debugging bad search results from “concept search” using LSI. You’ll find the equations for the technique as well as example Python code.
While researching Word2Vec, I came across a lot of different resources of varying usefullness, so I thought I’d share my collection of links and notes on what they contain.
This tutorial covers the skip gram neural network architecture for Word2Vec. My intention with this tutorial was to skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details. Specifically here I’m diving into the skip gram neural network model.