Category
embeddings
2 articles in this category
Why Embeddings Matter: Solving Key Issues in Data Representation
TLDR: Embeddings convert words (and images, users, products) into dense numerical vectors in a geometric space where semantic similarity = geometric proximity. "King - Man + Woman ā Queen" is not magic ā it is the arithmetic property of well-trained ...
ā¢5 min read
A Beginner's Guide to Vector Database Principles
TLDR: A vector database stores meaning as numbers so you can search by intent, not exact keywords. That is why "reset my password" can find "account recovery steps" even if the words are different. š Searching by Meaning, Not by Words A standard d...
ā¢6 min read