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Answer: Set the early stopping parameter to TRUE., Decrease the maximum number of trials during subsequent training phases.
The correct answers are C and E. Setting the early stopping parameter to TRUE (C) allows the hyperparameter tuning process to terminate trials early if they are not producing promising results. This prevents wasting computational resources on unpromising trials and can significantly speed up the hyperparameter tuning. Decreasing the maximum number of trials during subsequent training phases (E) can also help reduce the overall time taken for hyperparameter tuning without significantly impacting the effectiveness. This is because, after a certain point, additional trials often have diminishing returns on performance improvement. Other options like decreasing the number of parallel trials (A) would actually increase the time taken, and changing the search algorithm from Bayesian search to random search (D) may not be as effective in finding the best hyperparameters.
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You have developed and deployed an end-to-end machine learning pipeline that includes several stages, with hyperparameter tuning being one of the critical steps. Utilizing Google AI Platform, you perform hyperparameter tuning to optimize your model's performance before proceeding to the training phase. Recently, you observed that the hyperparameter tuning process is taking significantly longer than anticipated, causing delays in your overall workflow and downstream processes. To minimize these delays, you aim to accelerate the hyperparameter tuning job without significantly compromising its effectiveness. Which actions should you take? (Choose two.)
A
Decrease the number of parallel trials.
B
Decrease the range of floating-point values.
C
Set the early stopping parameter to TRUE.
D
Change the search algorithm from Bayesian search to random search.
E
Decrease the maximum number of trials during subsequent training phases.