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NEW QUESTION: 1
A security administrator wants to implement strong security on the company smart phones and terminal servers located in the data center.
Drag and drop the applicable controls to each asset types?
Instructions: Controls can be used multiple times and not all placeholders need to be filled. When you have completed the simulation, please select the Done button to submit.
Answer:
Explanation:
NEW QUESTION: 2
What are the two main forms of location update? (Multiple choice)
A. Periodic location update
B. Mandatory location update
C. Automatic location update
D. Implicit location update
Answer: A,B
NEW QUESTION: 3
Who is going to issue project charter?
A. project manager
B. functional manager
C. sponsor
D. stakeholder
Answer: C
Explanation:
Role of the sponsor is to issue project charter.
NEW QUESTION: 4
Which of the following are examples of hyperparameters? (Select 2 answers.)
A. Weights
B. Number of nodes in each hidden layer
C. Biases
D. Number of hidden layers
Answer: B,D
Explanation:
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview
