MauryaHub PYQ Practice

cs3004_2025T2_Q1_NA.pdf

Deep Learning · Quiz 1 · May 2025

← Course papers · Start practice / exam

Questions and published explanations below are available without starting a test. Some questions may not have a published solution yet.

Question 59 SHORT_TEXT · 0.0 marks

Have you opt for this **course**?
  1. yes
  2. no

A published solution is not available for this question yet.

Question 62 MCQ · 4.0 marks

[[IMAGE:ce66b7e255fed928_3_0]]
Source diagram or notation
  1. True
  2. False
  3. Insufficient data to arrive at a conclusion

A published solution is not available for this question yet.

Question 63 MCQ · 4.0 marks

[[IMAGE:ce66b7e255fed928_3_1]] Which of the following is true?
Source diagram or notation
  1. a > b
  2. a < b
  3. a = b

A published solution is not available for this question yet.

Question 64 MSQ · 4.0 marks

[[IMAGE:ce66b7e255fed928_4_2]]
Source diagram or notation
  1. D2 is linearly separable
  2. The perceptron learning algorithm will converge on D2
  3. D1 is linearly separable
  4. The perceptron learning algorithm will converge on D1

A published solution is not available for this question yet.

Question 65 NAT · 2.0 marks

[[IMAGE:ce66b7e255fed928_5_3]]
Source diagram or notation

    A published solution is not available for this question yet.

    Question 66 NAT · 2.0 marks

    To learn the parameters of a neural network for a classification problem, mini-batch gradient descent is run on a dataset of size 1000 with batch size of 25 for 10 epochs. Find the number of times each parameter is updated.

      A published solution is not available for this question yet.

      Question 67 MSQ · 2.0 marks

      [[IMAGE:ce66b7e255fed928_6_4]] Based on the above data, answer the given subquestions.
      If θ = 1, which of the following are true?
      Source diagram or notation
      1. h is the OR function
      2. h is a linearly separable Boolean function
      3. h is the AND function
      4. h is neither OR nor AND
      5. h is not a linearly separable Boolean function

      A published solution is not available for this question yet.

      Question 68 NAT · 2.0 marks

      [[IMAGE:ce66b7e255fed928_6_4]] Based on the above data, answer the given subquestions.
      [[IMAGE:ce66b7e255fed928_6_5]]
      Source diagram or notationSource diagram or notation

        A published solution is not available for this question yet.

        Question 69 NAT · 4.0 marks

        [[IMAGE:ce66b7e255fed928_6_4]] Based on the above data, answer the given subquestions.
        [[IMAGE:ce66b7e255fed928_7_6]]
        Source diagram or notationSource diagram or notation

          A published solution is not available for this question yet.

          Question 70 NAT · 4.0 marks

          Consider a neural network for a regression problem with one input and one output. There is one hidden layer with two sigmoid neurons. The output layer is linear. Ignore biases in all units. [[IMAGE:ce66b7e255fed928_8_7]] Based on the above data, answer the given subquestions.
          [[IMAGE:ce66b7e255fed928_8_8]]
          Source diagram or notationSource diagram or notation

            A published solution is not available for this question yet.

            Question 71 NAT · 4.0 marks

            Consider a neural network for a regression problem with one input and one output. There is one hidden layer with two sigmoid neurons. The output layer is linear. Ignore biases in all units. [[IMAGE:ce66b7e255fed928_8_7]] Based on the above data, answer the given subquestions.
            [[IMAGE:ce66b7e255fed928_9_9]]
            Source diagram or notationSource diagram or notation

              A published solution is not available for this question yet.

              Question 72 NAT · 4.0 marks

              [[IMAGE:ce66b7e255fed928_10_10]] Based on the above data, answer the given subquestions.
              Find the number of weights in the network.
              Source diagram or notation

                A published solution is not available for this question yet.

                Question 73 NAT · 4.0 marks

                [[IMAGE:ce66b7e255fed928_10_10]] Based on the above data, answer the given subquestions.
                If all the weights in the network have the same value, find the cross entropy loss for an arbitrary data-point. If this can be computed, enter the value of the loss correct to two places after the decimal. If the information provided is not sufficient to compute the loss, enter −1 as the answer.
                Source diagram or notation

                  A published solution is not available for this question yet.

                  Question 74 NAT · 4.0 marks

                  [[IMAGE:ce66b7e255fed928_11_11]] Based on the above data, answer the given subquestions.
                  [[IMAGE:ce66b7e255fed928_11_12]]
                  Source diagram or notationSource diagram or notation

                    A published solution is not available for this question yet.

                    Question 75 NAT · 6.0 marks

                    [[IMAGE:ce66b7e255fed928_11_11]] Based on the above data, answer the given subquestions.
                    [[IMAGE:ce66b7e255fed928_12_13]]
                    Source diagram or notationSource diagram or notation

                      A published solution is not available for this question yet.