Embeddings are numerical representations of data—such as words, sentences, or images—that capture their semantic meaning in a high-dimensional vector space. They are commonly used in machine learning and natural language processing to enable algorithms to understand and process complex, unstructured data. For example, word embeddings like Word2Vec or GloVe map words with similar meanings to nearby points in the vector space, allowing models to perform tasks such as sentiment analysis, translation, and question answering more effectively. By converting raw data into a format that machines can analyze, embeddings play a crucial role in improving the performance and accuracy of AI systems.
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