Case context: You are training a large image recognition model. Your initial model shows a high error rate on the training set. You have access to a massive, unlabelled repository of images that can be labeled at a cost, as well as significant computational resources.

Question: Based on modern deep learning principles, what two primary actions should you take in sequence to address the high training error, and then what should you do if the model subsequently performs poorly on the dev set?

Sample answer: First, to address the high training error (high bias), I should increase the neural network size and carefully tune the regularization method. Once the bias is reduced without noticeably increasing variance, I should evaluate the dev set. If the dev set error is high (high variance), I should utilize the repository by labeling and adding more training data to the model.

Key points:

• Diagnose high training error as high bias.
• Diagnose high dev set error as high variance.
• Increase network size and tune regularization for bias.
• Add training data to resolve variance.

Rubric: The response must correctly diagnose high training error as bias and high dev set error as variance. It must propose increasing network size and tuning regularization for the former, and adding training data for the latter.