Dimension reduction technique. For word vectors, the usual use case is to take the 400 dimension vector and turn it into a 2 dimension vector that you can use t

Dimension reduction technique. For word vectors, the usual use case is to take the 400 dimension vector and turn it into a 2 dimension vector that you can use to plot like a scatterplot. Similar things will be close together. Dissimilar things will be far apart. It's kind of like principal components analysis but quirkier. For those who like Tsne. Check out the relatively new UMAP, which seems to be faster and better.