Understanding Distance Metrics
Tim and Eric delve into the nuances of distance metrics, discussing the effectiveness of triplet loss and the challenges of using centroids in nonlinear spaces. They debate the relevance of computing centroids with nonlinear metrics and emphasize the importance of point-to-point distance comparisons for accurate clustering.In this clip
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Machine Learning Street Talk (MLST)
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