Embeddings
Embeddings are numerical representations of discrete data (like words, images, or users) in a continuous vector space where semantically similar items are located closer together. These high-dimensional vectors capture contextual meaning and relationships, allowing algorithms to process and understand complex data types effectively. They translate abstract concepts into a mathematical format that models can use.
Think of it as converting every unique item in a language or image into a precise coordinate on a multi-dimensional map, where similar items are clustered near each other.
Embeddings enable AI models to process and understand complex, non-numerical data like text and images by turning them into meaningful numerical representations.
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