Case context: You are working on a computer vision task using a standard neural network. Your model is suffering from high bias, but your previous attempts to simply increase model size led to massive variance. You are considering completely changing the neural network architecture.

Question: Based on the text, what should you expect regarding the predictability and difficulty of this approach, and where could you look for new architectures?

Sample answer: Changing the architecture can potentially address both the bias and variance issues simultaneously. However, you should expect this approach to be harder to identify and implement, and the results will be less predictable than just increasing the network size. You should look to academic literature and open-source implementations on GitHub for inspiration on innovative architectures.

Key points:

• Can affect both bias and variance simultaneously
• Less predictable than simple formulas
• Harder to identify and implement
• Use academic literature or open-source GitHub implementations for inspiration

Rubric: The learner must note the potential to affect both bias/variance, the unpredictability/difficulty of the approach, and suggest sources like literature or GitHub.