Selecting a Task Under Data Constraints
Case context: You are building a computer vision pipeline with a very limited budget for manually labeling training data. You must decide whether to train a component to classify if an image simply 'contains a bird' or to classify if it 'contains a Northern Cardinal'.
Question: Based on the principles of task simplicity, which classification subtask should you select for this component given your data constraints, and why?
Sample answer: You should select the subtask to classify whether the image 'contains a bird.' Classifying the general presence of a bird is an easier binary classification task than identifying a specific species. Because easier tasks can be learned with fewer training examples, it is the only feasible option given the limited budget for labeling data.
Key points:
- Select the 'contains a bird' task.
- Identify it as an easier task than species-level identification.
- Explain that easier tasks require fewer training examples, satisfying the constraint.
Rubric: The response must correctly choose the easier task ('contains a bird') and justify the choice by explaining that easier tasks require fewer training examples, which aligns with the limited data labeling budget.
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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)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Python Programming Language
Data Science
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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