Scoring Function
A scoring function can assign a score to each possible output for a given input, and an inference system can then search for the output that maximizes that score. A common AI design pattern is to learn an approximate scoring function and then apply an approximate maximization algorithm.
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References
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
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)
Machine Learning Yearning (Deeplearning.ai)
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Data Science
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Machine Learning Yearning @ DeepLearning.AI
Learn After
Approximate Search in Scored Inference
Distance-Based Scoring Function
Semantic Matching Scoring Function
In Andrew Ng's speech recognition example, what does Score_A(S) represent?
True or False: A scoring function alone is sufficient; no separate search or maximization step is needed.
An inference system searches for the output that _____ the scoring function.
Match each scoring function component to its role in the speech recognition example.
Order the steps of the scoring function design pattern in AI system design.
Explain why learning a scoring function and searching separately is a useful AI design pattern.
Diagnose a speech recognition error using the scoring function framework.
What two components make up the common AI design pattern described for scoring functions?
When can you apply the Optimization Verification test according to the source?
True or False: In the common design pattern, both the scoring function and the maximization algorithm are typically exact rather than approximate.