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This video tutorial discusses word and sentence embeddings, crucial for large language models. These embeddings convert words into numerical representations, allowing computers to process language, which is inherently made of words. Both word and sentence embeddings map words and sentences to lists of numbers semantically. This process is not human-driven; instead, it relies on complex neural networks that analyze context. When two words appear together in similar contexts, their numerical representations are adjusted to be closer. This method results in effective representations of language, leading to impressive outcomes in language processing.
