In the cat-detector example, why does Machine Learning Yearning recommend excluding scanned historical documents that look nothing like the dev/test distribution?

True or False: Adding more training data always improves model performance when training, dev, and test sets share the same distribution.

According to Machine Learning Yearning, if the dev error curve has _____ (i.e., flattened out), adding more training data will not help you reach your performance goal.

Why does Machine Learning Yearning recommend leaving out training data that has no benefit for your model?

True or False: According to Machine Learning Yearning, adding more training data can actually hurt model performance.

If the dev error curve has _____, you can immediately tell that adding more training data won't help reach your performance goal.

Match each concept to its correct description in Machine Learning Yearning's discussion of when adding training data does not help.

Order the steps for using a learning curve to decide whether to collect more training data, as described in Machine Learning Yearning.

In the cat-detector example, why should a large collection of scanned historical documents be excluded from training?

True or False: According to Machine Learning Yearning, inspecting the learning curve can prevent wasting months collecting data that turns out not to help.

According to Machine Learning Yearning, data that has no _____ should be left out of training for computational reasons.

Match each training data scenario to the recommended action from Machine Learning Yearning.

Order the reasoning steps for evaluating whether a new data source (e.g., scanned documents) should be added to training, per Machine Learning Yearning.

Analyzing Computational and Representational Costs of Unhelpful Training Data

Evaluating the Inclusion of Historical Document Scans in a Casual Cat-Detector Model

Neural Network Capacity and Irrelevant Training Data