Embeddings

Embeddings are a type of representation learning technique used in machine learning and natural language processing. They convert high-dimensional data into lower-dimensional vectors, capturing semantic relationships and similarities between items. Commonly used in applications like word embeddings, image embeddings, and recommendation systems, embeddings facilitate tasks such as clustering, classification, and information retrieval. By representing complex data in a continuous vector space, embeddings enable models to understand and manipulate data more effectively, improving performance in various AI applications.