Topic 1 Question 63
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
Hyperband
Grid search
Bayesian optimization
Random search
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コメント(2)
- 正解だと思う選択肢: A
https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-now-provides-up-to-three-times-faster-hyperparameter-tuning-with-hyperband/ "efficient resource utilization and a better time-to-convergence."
👍 1GiorgioGss2024/11/27 - 正解だと思う選択肢: A
A. Hyperband: Efficient & best --> Right answer B. Grid Search: Exhaustive and tries all combos C. Bayesian Optimization: Smart with best combination D. Random Search: Random
👍 1Saransundar2024/12/04
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