Definition:
Embeddings /ɛmˈbɛ.dɪŋz/ noun (plural) — In artificial intelligence and machine learning, embeddings refer to dense, continuous vector representations of discrete data elements such as words, images, or items. Unlike sparse one-hot encodings, embeddings encode semantic relationships by mapping input data into a lower-dimensional space where similar entities are placed close together.
For instance, in natural language processing (NLP), word embeddings like Word2Vec, GloVe, or BERT capture syntactic and semantic relationships between words based on context. This enables machines to process language more effectively, identifying meanings, analogies, and relevance.
Embeddings are fundamental in various applications, including search engines, chatbots, recommendation systems, and image recognition, allowing for efficient computation, improved generalization, and deeper understanding of complex data.
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