Learning Word Embedding
lilianweng.github.io- Category
- Other
- Type
- ARTICLE
- Builder
- @lilianweng
- Added
- Jul 21, 2026
About
Human vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).
Why it made the leaderboard
A clear walkthrough of how free-text words become numeric vectors — from one-hot encoding to learned embeddings — grounding the intuition behind the embedding models and vector search that agentic engineers rely on daily.
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