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Quiz on Optimizers in Deep Learning

Quiz on Optimizers in Deep Learning

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Question 1 of 10

1. Question

What is the main purpose of an optimizer in deep learning?

To initialize weights randomly
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To reduce the size of the model
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To update weights and minimize loss
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To visualize training progress
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Question 2 of 10

2. Question

Which of the following is a basic optimization algorithm?

Decision Tree
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Gradient Descent
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Naive Bayes
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Random Forest
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Question 3 of 10

3. Question

What does SGD stand for?

Stochastic Gradient Descent
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Sequential Gradient Direction
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Static Gradient Derivative
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Standard Gain Drop
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Question 4 of 10

4. Question

Which optimizer combines momentum and adaptive learning rates?

Adagrad
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RMSProp
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Adam
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Ndam
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Question 5 of 10

5. Question

What is the role of the learning rate in an optimizer?

It controls the model size
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It decides how fast weights are updated
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It adjusts the dataset size
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It determines the number of layers
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Question 6 of 10

6. Question

Which optimizer maintains a moving average of past gradients?

Adagrad
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SGD
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Adam
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KNN
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Question 7 of 10

7. Question

Which optimizer is often used for sequence data and RNNs?

RMSProp
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Logistic Regression
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SVM
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PCA
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Question 8 of 10

8. Question

What can happen if the learning rate is too high?

The model will train slowly
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The model will overfit
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The model may never converge
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The loss will stay constant
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Question 9 of 10

9. Question

Which optimizer uses a separate learning rate for each parameter?

SGD
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Adagrad
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Linear Regression
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SVM
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Question 10 of 10

10. Question

Which optimizer is widely used as the default choice in many deep learning projects?

Adam
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SGD
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Tanh
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ReLU
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