Embeddings in the context of Generative AI refer to the mathematical representation of complex data types, such as words, sentences, or even entire documents, in a high-dimensional space. These representations, often in the form of vectors, capture the semantic or contextual meaning of the data. Embeddings are a crucial part of many Machine Learning models, as they allow these models to understand and process data in a more human-like way. They are particularly useful in Natural Language Processing, recommendation systems, and other areas where understanding the relationships between different pieces of data is important.
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