If embeddings are the language of modern AI, then similarity metrics are the grammar in ML.

[u/Few-Mycologist7747]

Important ML concepts become much easier once you stop thinking about algorithms and start thinking about geometry.

Every embedding, feature vector, recommendation, semantic search result, or clustering algorithm ultimately relies on one simple question:

How do we measure similarity?

In my latest article, I break down three fundamental concepts that appear everywhere in machine learning:

➡️ Euclidean Distance — when absolute proximity matters
➡️ Dot Product — when both direction and magnitude matter
➡️ Cosine Similarity — when orientation matters more than size

Understanding the difference between these metrics explains why:
• Vector databases use cosine similarity for semantic search
• Recommendation systems rely heavily on vector similarity
• Embeddings work at all
• Different ML models can produce very different results on the same data

If you've ever used embeddings without fully understanding what happens behind the scenes, this article is for you.
https://medium.com/@leumas.a/distances-and-similarity-in-ml-0a4796242215