Vector embeddings: Difference between revisions

==Introduction==

[[Vector embeddings]] are lists of numbers used to represent complex data like [[text]], [[images]], or [[audio]] in a numerical format enabling [[machine learning algorithms]] to process them. These embeddings translate [[semantic similarity]] between objects into proximity within a [[vector space]], making them suitable for tasks such as [[clustering]], [[recommendation]], and [[classification]]. [[Clustering algorithms]] group similar points together, [[recommendation systems]] find similar objects, and [[classification tasks]] determine the label of an object based on its most similar counterparts.

Even if embeddings are not directly used for an application, many popular machine learning models and methods rely on them internally. For instance, in encoder-decoder architectures, the embeddings generated by the encoder contain the required information for the decoder to produce a result. This architecture is widely employed in applications like machine translation and caption generation.