Embedding Space
Embedding Space is a mathematical representation of objects or concepts in a lower-dimensional space, where each dimension represents a feature or attribute of the object or concept. In Data Science and Artificial Intelligence, embedding space is commonly used to represent words, images, or other types of data in a way that captures their semantic or contextual relationships. For example, in Natural Language Processing, words can be represented as vectors in an embedding space, where words with similar meanings are closer together in the space. This allows algorithms to better understand the meaning of words and make more accurate predictions. Similarly, in Computer Vision, images can be represented as vectors in an embedding space, where similar images are closer together. This allows algorithms to recognize patterns and classify images more accurately.
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