Question: Explain how the availability of plentiful data and large neural networks has changed the traditional perspective on the bias-variance tradeoff. Give specific examples of how practitioners can manipulate bias and variance independently.

Sample answer: In traditional machine learning, decreasing bias almost always increased variance, and vice versa. However, in modern deep learning, access to plentiful data and large neural networks softens this tradeoff. Practitioners can now often reduce bias without significantly increasing variance by increasing the network size and carefully tuning regularization. Conversely, they can reduce variance without hurting bias simply by adding more training data.

Key points:

• Plentiful data and large networks make the tradeoff less severe.
• Reduce bias by increasing network size.
• Use regularization to prevent variance increase when scaling the network.
• Reduce variance independently by adding training data.

Rubric: The essay should clearly contrast the traditional tradeoff with the modern deep learning approach, explicitly mentioning the use of larger networks with tuned regularization to reduce bias, and adding data to reduce variance.