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This video tutorial discusses word and sentence embeddings, essential components of large language models. Language models aim to help computers understand and process human language, which consists of words, while computers operate on numerical data. Word embeddings convert words into a numerical representation, linking each word to a list of numbers, while sentence embeddings do the same for sentences. This process is not manually done; instead, it is performed by a neural network that analyzes context. When two words frequently appear together in similar contexts, the model adjusts their numerical representations to bring them closer together. This results in meaningful embeddings with impressive outcomes.
