Notes on machine learning
Technical notes written while learning: honest evaluation, data leakage, deployment, LLM applications and the mistakes behind each lesson. They come from the projects on this site, with the code one click away.
BM25 versus embeddings: I added semantic search to my search (with real numbers)
I added an optional toggle to Ask that re-ranks BM25 results with MiniLM embeddings. Before turning it on by default I tried 15 real questions in both languages: it won clearly in 1, helped a little in 2, tied in 8, lost outright in 1, and both approaches missed in 3. This is the full count, not rounded up.
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Everything that happens when you press Enter in my search
The technical version: from the key event to the ranking, with the 735-fragment index, BM25 with k1=1.5 and b=0.75, the language boost and the decisions made along the way. For readers of the jargon-free version who want to see the gears.
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RAG: verifiable answers over your documents
How retrieval-augmented generation works: indexing, semantic retrieval, chunking and evaluation. Why every answer should be able to cite where it comes from …
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