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Quiz on Adam Optimizers in Deep Learning

Quiz on Adam Optimizers in Deep Learning

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Question 1 of 10

1. Question

What does “Adam” stand for in deep learning?

Adaptive Module
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Advanced Decay Algorithm Method
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Adaptive Moment Estimation
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Automatic Derivative Approach Method
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Question 2 of 10

2. Question

Which two optimizers’ concepts are combined in Adam?

SGD and Batch Norm
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Momentum and RMSProp
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ReLU and Tanh
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Adagrad and Dropout
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Question 3 of 10

3. Question

What does beta1 control in Adam optimizer?

Learning rate decay
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Step size for weight update
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Exponential decay for moving average of gradients
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Output activation function
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Question 4 of 10

4. Question

What is the default value of learning_rate in Adam?

0.01
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0.1
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0.5
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0.001
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Question 5 of 10

5. Question

Which parameter in Adam helps avoid division by zero?

alpha
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gamma
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epsilon
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lambda
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Question 6 of 10

6. Question

Why is Adam preferred over SGD in most deep learning problems?

Requires more GPU memory
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Slower but more accurate
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Adapts learning rates automatically for each parameter
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Works only with small datasets
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Question 7 of 10

7. Question

What does beta2 represent in Adam?

Dropout probability
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Momentum for squared gradients
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Learning rate decay
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Batch size
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Question 8 of 10

8. Question

Which of the following is a common use case of the Adam optimizer?

Random Forest tuning
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Model compression
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Deep neural network training
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Dataset labeling
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Question 9 of 10

9. Question

If a model is not converging using Adam, what should you try first?

Increase batch size
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Increase dropout rate
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Reduce the learning rate
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Remove all hidden layers
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Question 10 of 10

10. Question

What is the major strength of the Adam optimizer?

No need for tuning
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Handles large and noisy datasets efficiently
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Works only with CNNs
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Only suitable for binary classification
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