Google Professional Machine Learning Engineer
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In the context of deploying Dense Neural Network (DNN) models for a variety of tasks, your team has automated the release process. However, to ensure these models are not only automatically released but also sufficiently complex for their intended tasks, you need to implement a robust verification strategy. Considering factors such as computational efficiency, benchmarking against simpler models, and the ability to generalize, which of the following strategies is the most effective? Choose one correct option.
Explanation:
The correct approach is to train a simple linear model and compare its performance to the DNN model. This method is effective because it provides a clear benchmark to evaluate the DNN's performance, ensuring the model's complexity is adequate for the task. Linear models are quicker to train, making this a practical test for complexity. Other options, such as checking for NaN values or verifying a constant loss, do not directly assess whether the DNN's complexity is adequate for the task. Similarly, training without regularization can indicate capacity but lacks a comparative benchmark.