Understanding Vector Embeddings
Dharmesh breaks down the concept of vector embeddings by illustrating how points in multi-dimensional space can represent the meaning of written content. He emphasizes the significance of measuring semantic distance, allowing for connections between concepts that use different language. This approach opens up new opportunities in AI, particularly in moving beyond outdated keyword-based matching systems.In this clip
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