Embedding
A dense numerical representation of data, such as words or images, that captures meaningful relationships and semantic similarities in a lower-dimensional space.
What is an Embedding?
Embeddings transform complex or high-dimensional data into dense numerical vectors that machine learning models can process. Items with similar meanings or characteristics are typically positioned closer together within the embedding space. For example, words with related meanings may have similar vector representations. Embeddings can be learned during model training or generated using pre-trained models.
Why are Embeddings Important?
Embeddings allow AI systems to represent semantic relationships in a format that computers can efficiently compare and analyze. They are especially important for modern search, recommendation, and generative AI systems, where understanding similarity and context is essential.
Common use cases
Embeddings are commonly used in semantic search, RAG systems, recommendation engines, text classification, clustering, image retrieval, and natural language processing.