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This video tutorial discusses word and sentence embeddings, which are essential for large language models. Language models aim to enable computers to understand and process human language. Since language is composed of words and computers handle numbers, word embeddings translate words into numerical representations, while sentence embeddings do the same for sentences. This process is not manually done by humans; instead, it is performed by a neural network that analyzes context. When two words frequently appear together, their embeddings are adjusted to be closer in the numerical space. The same principle applies to sentences, leading to impressive results in language processing.
