What are Embeddings

Embeddings are dense, numerical vector representations of data, such as words, sentences, images, or audio, that capture their semantic meaning and relationships. By converting data into fixed-length vectors, embeddings allow machine learning models to process and understand the data more effectively. For example, word embeddings represent similar words with similar vectors, enabling tasks like semantic search, recommendation systems, and clustering. Embeddings make it easier to compare, search, and analyze complex, unstructured data by mapping similar items close together in a high-dimensional space.

Visit the following resources to learn more:

• @article@What is Embedding? -IBM
• @article@What are Embeddings in Machine Learning?
• @video@What are Word Embeddings