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Deep Learning · Quiz 2 · Sep 2022

Deep Learning · Quiz 2 · Sep 2022

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Question 157 SHORT_TEXT · 1.0 marks

List the horizon nodes assigned SOLVED status by the SSS* algorithm. Enter the node labels as a comma separated list in ASCENDING order. NO SPACES, TABS, DOTS, BRACKETS OR EXTRANEOUS CHARACTERS. Answer format: X,Y,Z

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Question 158 MCQ · 0.0 marks

Source diagram or notation
6406531487420.
YES
6406531487421.
NO

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Question 159 NAT · 3.0 marks

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Based on the above data, answer the given subquestions.
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Question 160 NAT · 2.0 marks

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Based on the above data, answer the given subquestions.
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Question 161 NAT · 10.0 marks

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Based on the above data, answer the given subquestions.
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Question 162 NAT · 5.0 marks

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Based on the above data, answer the given subquestions.
Suppose we replace the sigmoid (logistic) activation function by ReLU and max pooling by min pooling (that is, output the minimum value from the input tensor). Update the parameters and enter the sum of updated parameter values?. The ReLU is defined as follows
Source diagram or notation
(If your answer is 0.3345, enter it as 0.33)

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Question 163 NAT · 2.0 marks

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Based on the above data, answer the given subquestions.
The dimension of C1 = 101 × 101 × 100. Suppose we use 30 kernels (filters) of shape 3 × 3, with stride 1 and no zero padding, to generate the convolution layer 2. Then, enter the number of parameters (including bias) in the convolution layer-2 (If your answer is 1050,enter 1050)

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Question 164 NAT · 2.0 marks

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Based on the above data, answer the given subquestions.
We know that the shape of the flattened layer is N ×1. What is the value of N?(If your answer is 1050,enter 1050)

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Question 165 NAT · 2.0 marks

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Based on the above data, answer the given subquestions.
Suppose we apply three 1D kernels after the pooling layer, P2, and then flatten the final output to the shape of N × 1. What is the value of N now?

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Question 166 NAT · 3.0 marks

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Based on the above data, answer the given subquestions.
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Question 167 NAT · 3.0 marks

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Based on the above data, answer the given subquestions.
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Question 168 MCQ · 2.0 marks

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Based on the above data, answer the given subquestions.
The statement “Using sigmoid activation function for a neuron in a neural network always shrinks the gradient passing through it during backpropagation ” is
6406531487451.
TRUE
6406531487452.
FALSE

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Question 169 MCQ · 2.0 marks

The diagram below shows the contours of a loss surface generated by the following model,
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where, m is the number of training samples, x1 and x2 are features and y is the corresponding ground truth. Assume that both features x1 and x2 are normalized to be in the same range.
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Which of the following statement(s) is (are) true?
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x1 is sparse, x2 is dense
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x1 is dense, x2 is sparse
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both x1 and x2 are sparse
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both x1 and x2 are dense

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Question 170 MCQ · 6.0 marks

The update rule for the ADAM (Adaptive Moments) optimization algorithm is given below,
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6406531487437.Source diagram or notation

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Question 171 MSQ · 4.0 marks

Source diagram or notation
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RMSProp
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NADAM
6406531487440.
NAG with cyclic learning rate
6406531487441.
GD with exponential weight decay learning rate scheduler

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Question 172 MSQ · 4.0 marks

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      **SPG** **Section Id :** 64065329028 **Section Number :** 9 **Section type :** Online **Mandatory or Optional :** Mandatory **Number of Questions :** 36 **Number of Questions to be attempted :** 36 **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

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