Case context: A product team is comparing two vision tasks. One task is to decide whether a warehouse photo was taken in daylight or at dusk, and a compact model with only one hidden layer performs well. The other task is to identify 500 different storefront sign categories in crowded street scenes with shadows, reflections, and partial occlusion, and the team finds that a much deeper model is needed to reach acceptable performance.

Question: Using the informal idea of task difficulty in machine learning, which task is easier and which is harder, and what reasoning supports that conclusion?

Sample answer: The daylight-versus-dusk task is easier because it can be solved with relatively few processing steps, so a shallow neural network is sufficient. The storefront-sign task is harder because it requires many more processing steps, which is why a deeper neural network is needed.

Key points:

• The first task should be labeled easy.
• Easy tasks can often be handled with fewer computation steps and shallow networks.
• The second task should be labeled hard.
• Hard tasks generally need more computation steps and deeper networks.

Rubric: The answer must identify the first task as easy and the second task as hard, and it must connect those labels to the use of shallow and deep neural network architectures, respectively.