PROFESSIONAL-DATA-ENGINEER · Question #82
Which of the following are examples of hyperparameters? (Select 2 answers.)
The correct answer is A. Number of hidden layers B. Number of nodes in each hidden layer. Explanation/Reference: 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
Question
Options
- ANumber of hidden layers
- BNumber of nodes in each hidden layer
- CBiases
- DWeights
How the community answered
(33 responses)- A94% (31)
- C3% (1)
- D3% (1)
Explanation
Explanation/Reference: 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.
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