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Module 6 · Deep Learning + MLOps
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Embeddings: cosine vs Euclidean
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📝 **Question:** Why prefer cosine for text embedding search? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
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📝 **Question:** Why prefer cosine for text embedding search? 📋 Pick the right answer. 💡 **Hint:** Re-read the theory above if unsure.
A
Cosine yields a bounded [-1,1] score that's directly interpretable as a percentage confidence value.
B
Cosine ignores magnitude — two embeddings with same meaning but different length still match.
C
Cosine sidesteps the curse of dimensionality because dot products concentrate around π/2 in high-d spaces.
D
Cosine is the only similarity metric supported by HNSW indexes in FAISS and pgvector libraries.
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