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EndsemMay 20252019 patternBE Comp

Deep Learning (DL) SPPU Endsem Question Paper May 2025

All 8 questions from this SPPU endsem paper, as printed. Read them here or open the PDF.

Open the PDFMost repeated DL questions
Subject code
410251
Pattern
2019 pattern
Exam
Endsem
Session
May 2025
Max. marks
70
Time
2½ Hours
Semester
VIII
Paper code
[6404]-95

Printed for: B.E (Computer Engineering)

Instructions to the candidates

  1. Solve Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6, Q.7 or Q.8.
  2. Figures to the right side indicate full marks.
  3. Neat diagrams must be drawn wherever necessary.
  4. Make suitable assumption whenever necessary.

Questions

  1. Q1

    1. a)

      Draw and Explain CNN Architecture in detail.

      [6]
    2. b)

      Explain working of Convolution Layer.

      Also asked:May 2023

      [6]
    3. c)

      Explain pooling layers and its type.

      Also asked:May 2024 ·Nov 2023 ·May 2023

      [6]

  2. Q2

    1. a)

      Explain all the features of pooling layer.

      Also asked:May 2023

      [6]
    2. b)

      Explain local response Normalization.

      Also asked:Nov 2023

      [6]
    3. c)

      Explain ReLU Layer in detail.

      [6]

  3. Q3

    1. a)

      Explain recursive neural network.

      Also asked:Nov 2023 ·May 2023

      [6]
    2. b)

      Explain the LSTM in RNN.

      [6]
    3. c)

      Explain in brief about working of RNN.

      [5]

  4. Q4

    1. a)

      Difference between CNN Vs RNN.

      [6]
    2. b)

      What are the challenges of long term dependencies?

      Also asked:Nov 2025

      [6]
    3. c)

      Explain Encoder-Decoder RNN model. 2

      [5]

  5. Q5

    1. a)

      Explain Deep Generative Model.

      Also asked:Nov 2025 ·May 2024 ·Nov 2023 ·May 2023

      [6]
    2. b)

      Explain Boltzmann Machine in details.

      [6]
    3. c)

      Explain in brief GAN with an example.

      [6]

  6. Q6

    1. a)

      Explain deep belief networks in detail.

      Also asked:Nov 2025 ·May 2024 ·Nov 2023 ·May 2023

      [6]
    2. b)

      What is Generative Adversarial Network? Explain its component.

      [6]
    3. c)

      Explain types of GAN.

      [6]

  7. Q7

    1. a)

      Explain Markov Decision Process.

      Also asked:Nov 2025 ·Nov 2023 ·May 2023

      [6]
    2. b)

      Explain deep reinforcement learning.

      Also asked:Nov 2023 ·May 2023

      [6]
    3. c)

      What are the challenges of reinforcement learning?

      Also asked:Nov 2023 ·May 2023

      [5]

  8. Q8

    1. a)

      Explain the process of Deep Q-learning.

      [6]
    2. b)

      Explain reinforcement learning for Tic-Tac-Toe game.

      Also asked:Nov 2025 ·Nov 2023 ·May 2023

      [6]
    3. c)

      This part has equations that don’t show as text. Open the PDF to read it.

      [5]