Embedding
A dense vector representation of discrete variables in a continuous vector space.
Description
In machine learning, an embedding is a way to represent discrete variables as continuous vectors in a lower-dimensional space. Embeddings capture semantic meanings and relationships between entities, making them particularly useful in natural language processing and recommendation systems. They allow models to work with high-dimensional categorical data more effectively by projecting it into a dense, continuous space where similar items are closer together.
Examples
- 📝 Word embeddings (e.g., Word2Vec, GloVe)
- 📊 Sentence embeddings
- 🕸️ Graph embeddings
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