Exam Professional Machine Learning Engineer topic 1 question 13 discussion

Should be C https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

increasing the size of the network will make the overfitting situation worse

As its DNN, dropout helps. L1 or L2 are for increasing generalization.

C is the best answers. You cannot increase neurons as the model is too complex already and cannot generalize!

Voted C

A,B have very specific numbers which doesn't gurantee success C is best D - increases the size - which is not helping with overfitting

Req: make model resilient A. Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10. : Might / might not work . But may not find optimal parameter set since it uses random values B. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10. : Might / might not work . But may not find optimal parameter set since it uses random values C. Run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters. : l2 and dropout are regularisation method which would work. Let AI find the optimal solution on how extend these parameters should regularise. Yes this would work. D. Run a hyperparameter tuning job on AI Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2 : AIplatform would do but adding neurons would make network nore complex. So we can eliminate this option.

It should be C as regularization (L1/L2), early stopping and drop out are some of the ways in deep learning to handle overfitting. Other options have specific values which may or may not solve overfitting as it depends on specific use case.

Went with C

ANS: C A and B are random values, why they choose that values? D could increase even more overfitting since you're using a more complex model.

We don't know the optimum values for the parameters, so we need to run a hyperparameter tuning job; L2 regularization and dropout parameters are great ways to avoid overfitting. So C is the answer

Correct answer is "C"

- by options eliminations C,D are better than A,D (more automated , scalable) - between C,D C is better as in D "and increase the number of neurons by a factor of 2" will make matters worse and increase overfitting

C for sure

Best practice is to let a AI Platform tool run the tuning to optimize hyperparameters. Why should I trust values in answers A or B?? Plus L2 regularization and dropout are the way to go here.

Community vote

it is the logical ans