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

Applications