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triplet loss

Applied AI interview questions tagged triplet loss, across every topic.

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Concepts behind "triplet loss"

The curriculum that explains the ideas these questions test.

Core
📊 Evaluation & ML FoundationsSign in
Contrastive and Metric LearningContrastive learning trains embeddings by comparison: pull similar (positive) pairs together and push dissimilar (negative) pairs apart, so distance encodes similarity. It powers retrieval embeddings, CLIP's shared text-image space, face recognition, and self-supervised pretraining. Quality hinges on the number and difficulty of negatives. Applied-AI interviews probe it because it is how the embeddings under search, RAG, and recommendation are actually trained, and because 'where do good embeddings come from?' has a concrete answer.
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