Published on: 2025-06-23 10:20:20
Published: Mar 8, 2025 at To understand how an AI can understand that the word “cat” is similar to “kitten,” you must realize cosine similarity. In short, with the help of embeddings, we can represent words as vectors in a high-dimensional space. If the word “cat” is represented as a vector [1, 0, 0], the word “kitten” would be represented as [1, 0, 1]. Now, we can use cosine similarity to measure the similarity between the two vectors. In this blog post, we will break down the concept of cosin
Keywords: cosine similarity veca vecb vectors
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