Embedding
Dense vector representations of data that capture semantic meaning in a form usable by machine learning models.
Detailed Explanation
Embeddings are dense vector representations that map high-dimensional data (like text, images, or audio) into a lower-dimensional space while preserving semantic relationships. In this vector space, similar items are positioned closer together. For example, word embeddings might place "king" and "queen" near each other because they share semantic properties. Embeddings are fundamental to modern AI because they transform raw data into a format that machine learning models can effectively process. They enable semantic search, recommendation systems, and many other applications by allowing computers to understand similarity and relationships between different pieces of information.
Examples
- Word embeddings like Word2Vec
- Sentence embeddings
- Image embeddings