It actually has a lot of overlap with deep learning. Here's an NLP example.

Lets say you have millions of phrases and you want to find the ones that are the most similar to a given one that you've chosen. One way of doing this would be to create embedding for each phrase and looking at some metric like cosine similarity of the embeddings to determine closeness of the phrases. The problem is, you don't want to have to compare the embedding of your phrase to every other phrase in your collection. In this case, LSH can help you narrow it down.

Malware detection uses it as a heuristic to peg specific “variants” to each other. Basically LSH is great for comparing corpuses of data that are slightly different.

If you hash the data using something like SHA256 and even one byte is different, it’ll produce a radically different hash (which is by design). With LSH you can have a measurement to say “corpus A has a 90% overlap with corpus B”, or in a single bit flip case, a very high correlation.

Image similarity.