A data scientist is implementing a deep learning neural network model for an object detection task on images. The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time.
The data scientist must ensure that jobs that underperform are stopped. The data scientist must allocate computational resources to well-performing hyperparameter configurations. The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size.
Which technique will meet these requirements with LEAST computational time?

A. Grid search

B. Random search

C. Bayesian optimization

D. Hyperband








 

Suggested Answer: D

Community Answer: D




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


Disclaimers:
The website is not related to, affiliated with, endorsed or authorized by Amazon.
Trademarks, certification & product names are used for reference only and belong to Amazon.
The website does not contain actual questions and answers from Amazon's Certification Exam.