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State the trade-off of using approximate search algorithms in scored inference.
Question: What is the primary trade-off we make when we apply an approximate search algorithm rather than exhaustive enumeration to optimize ScoreA(S)?
Sample answer: We trade the mathematical guarantee of finding the absolute maximum of ScoreA(S) for computational tractability, allowing us to process large search spaces that are otherwise too large to exhaustively enumerate.
Key points:
- Loss of the guarantee to find the output that maximizes ScoreA(S).
- Gain of computational tractability/feasibility in large output spaces.
Rubric: The student's answer must identify that we sacrifice the guarantee of finding the optimal output (the value of S that maximizes the score) in order to achieve computational tractability.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Data Science
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Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Machine Learning Strategy
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