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Question 105 MCQ · 1.0 marks
If the subgoals in the goal description are swapped places then does the Goal Stack Planning
algorithm find an optimal plan?
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Yes
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No
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Cannot be determined
**Deep learning**
**Section Id :** 64065333975
**Section Number :** 5
**Section type :** Online
**Mandatory or Optional :** Mandatory
**Number of Questions :** 12
**Number of Questions to be attempted :** 12
**Section Marks :** 50
**Display Number Panel :** Yes
**Group All Questions :** No
**Enable Mark as Answered Mark for Review and**
Yes
**Clear Response :**
**Maximum Instruction Time :** 0
A published solution is not available for this question yet.
Consider the assertion and the corresponding reason,
Assertion (A): The effective learning rate in RMSprop may increase or decrease
Reason (R): This is due to exponential averaging of squared gradients
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A is true but R is not the correct reason
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A is True and R is the correct reason
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A is False and therefore R
A published solution is not available for this question yet.
Suppose that we have a deep neural network with sigmoid neurons at all layers. Which of the
following approaches, in general, help the network to overcome the vanishing gradient problem?
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Replace sigmoid neuron by tanh
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Replace sigmoid neuron by GELU
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Use unsupervised layerwise pretraining and finally train the entire network
with supervised objective
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Use supervised training one-layer at a time with sigmoid functions and
gradually add layers
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Use Residual connections
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Constrain the weight values with an appropriate regularization technique
A published solution is not available for this question yet.
An input image of dimension 55 × 55 × 3 is to be convolved with 10 filters (kernels) of spatial
dimension 5 × 5. In all the sub-questions, assume a bias term for kernels.
Suppose that we pad zeros of size 1 (p = 1) around the sides of the image (in all channels). Assume
stride 2 for all 10 convolution filters on the input image. All the 10 filters have bias associated with
it. What is the dimension of the output volume? Enter the number of elements in the output
volume.
A published solution is not available for this question yet.
An input image of dimension 55 × 55 × 3 is to be convolved with 10 filters (kernels) of spatial
dimension 5 × 5. In all the sub-questions, assume a bias term for kernels.
How many number of parameters are there in the convolution layer?
A published solution is not available for this question yet.
An input image of dimension 55 × 55 × 3 is to be convolved with 10 filters (kernels) of spatial
dimension 5 × 5. In all the sub-questions, assume a bias term for kernels.
A published solution is not available for this question yet.
An input image of dimension 55 × 55 × 3 is to be convolved with 10 filters (kernels) of spatial
dimension 5 × 5. In all the sub-questions, assume a bias term for kernels.
Suppose we applied 1D convolution operation with 10 kernels on the output of the first
convolution layer and then applied the rest of the operations as in the previous question
(flattening followed by fully connected layer), then the number of parameters in the network
would have
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Increased
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Decreased
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Remained same
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Insufficient information
A published solution is not available for this question yet.
