Question: A team has about 750,000 labeled satellite tiles for a land-cover classifier. Write a concise explanation of why a neural network may be a sensible choice here.

Sample answer: A dataset that large can support training a high-capacity model. Neural networks can use many examples to learn complicated patterns and relationships that simpler models may miss, and the extra data helps limit overfitting. For that reason, a neural network is often a good fit when the training set is very large.

Key points:

• Hundreds of thousands of examples count as a very large dataset.
• High-capacity models benefit from abundant data.
• Large training sets reduce the risk that a complex model will overfit.

Rubric: A strong answer connects dataset scale to the ability of neural networks to learn complex structure and notes that abundant data makes a complex model more practical.