What are vector embeddings?
Embeddings turn concepts into coordinates.
How It Works:
Embeddings map items (words, images, users) into continuous vector spaces where similar items lie close together, learned via neural models.
Key Benefits:
- Captures semantic similarity
- Serves as compact features for downstream tasks
- Enables nearest-neighbor lookups
Real-World Use Cases:
- Document search with semantic relevance
- Product recommendations based on embedding proximity
FAQs
How large are embedding vectors?
Can I fine-tune embeddings?
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