If you find this stuff useful, I recommend also checking out our pygraphistry umap() for a few reasons here: - by visually linking similar entities, vs just a

If you find this stuff useful, I recommend also checking out our pygraphistry umap() for a few reasons here: - by visually linking similar entities, vs just a scatter plot of values floating around or binning, you see a lot of the substructure and non-linkages these tools otherwise mislead you on - you get a full interactive suite for on-the-fly visual encodings, drilling, reclustering, etc for wheb you start investigating it - automatic support for heterogenous data. Ex: for user analysis, combine profile + name embeddings with numeric, date, Boolean, etc features, so you can work with the real dataset, not just a couple columns For video/images, there are other tools I'd recommend because they add another layer of nuance. But for this kind of text, tabular, etc data, we've come to be opinionated and built in a lot here over the last few years, including with cool work by our collaborators at Nvidia (rapids)