[ML 2.a] Word2Vec: Start of Dense Embeddings
When you type a search query into Google or ask Spotify to find “chill acoustic covers,” the system doesn’t just look for those exact letters. It understands that “chill” is related to “relaxing” and…
Read More[ML 2.b] Measuring Meaning: Cosine Similarity
In the previous posts, we established that embeddings turn everything into points in space and that Word2Vec showed how to learn those points from context. But we glossed over something critical: how do you actually measure “closeness”? When…
Read More[ML 2.c] Needle in the Haystack: Embedding Training and Context Rot
You’ve probably experienced this: you paste a 50-page document into ChatGPT or Claude, ask a specific question about something buried on page 37, and the model either ignores it, gives a vague answer, or confidently cites something from page 2…
Read MoreHow Smart Vector Search Works
In the ever-evolving world, the art of forging genuine connections remains timeless. Whether it’s with colleagues, clients, or partners, establishing a genuine rapport paves the way for collaborative success.
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