Embeddings
Embeddings represent complex data as vectors, capturing semantic relationships in natural language processing and other AI applications.
Embeddings are a way to represent high-dimensional data in lower-dimensional spaces, typically used in natural language processing (NLP). Words, images, or other data points are transformed into vectors of real numbers, preserving semantic relationships in a compact form. Word2Vec, GloVe, and BERT are common embedding techniques used to capture word meanings and similarities for NLP tasks.