Tuning Auxiliary Data Weight with the Dev Set

Which condition makes it appropriate to down-weight auxiliary training data according to Andrew Ng?

Down-weighting auxiliary data can reduce the computational burden of training a model on both auxiliary and target-distribution examples.

Re-weighting is needed only when you suspect the additional data has a very _____ distribution than the dev/test set.

Match each training scenario to its key implication for handling auxiliary data.

Order the reasoning steps a practitioner should follow when deciding whether to down-weight large-scale auxiliary training data.

In the 40x internet vs. mobile image scenario, what is the main cost of training equally on both sources without down-weighting?

Giving auxiliary data a lower weight in training is equivalent to removing it from the training set entirely.

By giving internet images a much lower _____, you don't have to build as massive a neural network.

Match each concept to its correct description in the context of auxiliary data down-weighting.

Order the causal steps explaining why down-weighting auxiliary data allows a smaller neural network to be used.