Case context: You are training a deep neural network for a speech recognition system that exhibits high variance. Due to infrastructure limitations, training currently takes several days. The team needs to reduce variance to improve generalization but also desperately needs a faster training iteration cycle to meet a tight deadline.

Question: Based on Andrew Ng's recommendations, analyze whether you should decrease the model size or add regularization in this scenario, and justify your choice based on the project constraints.

Sample answer: In this scenario, decreasing the model size (e.g., reducing neurons or layers) is justified because speeding up training is a priority due to the tight deadline and limited infrastructure. Decreasing the model size will reduce computational cost and speed up training, while also helping to decrease variance (though it may increase bias). If training time and computational cost were not concerns, adding regularization would be the preferred recommendation because it typically provides better classification performance. However, because faster training is a key constraint here, reducing model size is a suitable compromise.

Key points:

• Identifies that decreasing model size reduces computational cost and speeds up training.
• Recognizes that regularization is the preferred variance remedy when computational cost is not a concern because it yields better classification performance.
• Justifies the decision to reduce model size based on the specific training speed and computational constraints in the scenario.

Rubric: The answer should evaluate both options (reducing model size and adding regularization) under the given constraints. It must identify that reducing model size is appropriate here because it addresses the training speed constraint, whereas regularization would not speed up training. It must also note that regularization is generally the superior variance remedy for classification performance when computational cost is not an issue.