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Optimizer in PyTorch QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

How does weight decay contribute to regularization in deep learning in PyTorch?

Question: 2

When might a cyclic learning rate schedule be beneficial in training a neural network?

Question: 3

What does the term "L2 regularization" refer to in the context of PyTorch?

Question: 4

What does the term "Adagrad" stand for in the context of PyTorch optimization?

Question: 5

In the context of learning rate schedules, what does the term "annealing" refer to?

Question: 6

Which optimizer is known for adapting the learning rates individually for each parameter in the neural network?

Question: 7

Apart from weight decay, what is another common form of regularization used in neural networks?

Question: 8

What is the primary advantage of using a learning rate schedule with a warm-up phase?

Question: 9

Which optimizer is known for being robust to noisy gradients and is suitable for training recurrent neural networks (RNNs)?

Question: 10

What is the purpose of a learning rate decay in training neural networks?

Question: 11

When training a deep neural network with a large batch size, which optimizer is generally more suitable?

Question: 12

How does early stopping relate to regularization in deep learning?

Question: 13

In PyTorch, which parameter is commonly used to control the strength of weight decay in an optimizer?

Question: 14

Which optimizer is suitable for non-convex optimization problems with noisy or sparse gradients?

Question: 15

What is the primary goal of dropout regularization in neural networks?

Question: 16

Which optimizer is suitable for training deep neural networks with sparse gradients?

Question: 17

What does the term "L2 regularization" refer to in the context of deep learning?

Question: 18

Which optimizer is commonly used for stochastic gradient descent with momentum in PyTorch?

Question: 19

Which PyTorch function is commonly used for learning rate scheduling?

Question: 20

What is the primary drawback of using too much weight decay in training?

Question: 21

Which optimizer in PyTorch is known for its effectiveness in training deep neural networks?

Question: 22

When training a large-scale neural network with limited computational resources, which optimizer might be a good choice due to its memory efficiency?

Question: 23

In PyTorch, how is weight decay commonly implemented when defining an optimizer?

Question: 24

Which optimizer is suitable for sparse gradients and adapts learning rates individually for each parameter in PyTorch?

Question: 25

Which optimizer is known for combining the benefits of both SGD and RMSprop?

Question: 26

What is the primary advantage of using learning rate schedulers in PyTorch?

Question: 27

Which PyTorch optimizer is suitable for non-stationary objectives and provides adaptive learning rates for each parameter?

Question: 28

Which PyTorch optimizer is designed to address the limitations of Adagrad by dynamically scaling the learning rates for each parameter?

Question: 29

Which optimizer is commonly used for training deep neural networks in PyTorch?

Question: 30

Which learning rate scheduling technique gradually increases the learning rate during training in PyTorch?