Question: Explain why a very large neural network by itself is not enough to achieve top performance. What else is needed, and how do the two factors work together?

Sample answer: A very large neural network provides the capacity to represent complicated patterns, but capacity alone does not guarantee strong performance. To use that capacity well, the model also needs a very large amount of training data. The data supplies enough varied examples for the network to learn reliable patterns instead of merely memorizing the training set. In practice, high performance comes from combining a high-capacity model with enough data to train it effectively.

Key points:

• A very large neural network gives high capacity.
• A very large amount of data is also required.
• Strong performance depends on using both together.

Rubric: The response must explicitly mention the need for a very large amount of data together with a large neural network. It should explain that both are needed to achieve the best performance.