Embeddings

Metadata

• Author: udara.io
• Full Title: Embeddings
• URL: https://udara.io/embeddings

Highlights

Embeddings are, at their core, numerical representations of information — such as words, sentences, or images — mapped into a continuous vector space. In other words, they translate data into a language that machines can understand and process efficiently. (View Highlight)

For example, cosine similarity measures how similar two embeddings are, irrespective of their magnitude. This operation is crucial in tasks like document clustering or finding words that share semantic meanings. (View Highlight)