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It is important what the numbers in a model mean. Embeddings capture the semantic relationships between entities as distance. This useful for machine learning because algorithms can learn how predict a label based on distance, thus learn how semantic relationships between entities are predictive.


No, creating dummy unknowns is not the best way to do it. A better approach can be, if a new face comes in, we calculate distance between vector of the new face and all of the vectors of known faces already present with us. And to identify the correct face, the minimum distance is considered. But this minimum distance should also be below a threshold value. ...

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