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InsemMar 20252019 patternBE Comp

Deep Learning (DL) SPPU Insem Question Paper Mar 2025

All 4 questions from this SPPU insem paper, as printed. Read them here or open the PDF.

Open the PDFMost repeated DL questions
Subject code
410251
Pattern
2019 pattern
Exam
Insem
Session
Mar 2025
Max. marks
30
Time
1 Hour
Semester
VIII
Paper code
[6411]-41

Printed for: B.E. (Computer Engineering) (Insem)

Instructions to the candidates

  1. Answer Q1 or Q2, Q3 or Q4.
  2. Figures to the right indicate full marks.
  3. Neat diagrams must be drawn wherever necessary.
  4. Assume suitable data, if necessary.

Questions

  1. Q1

    1. a)

      How Deep Learning works in three figures explain with example? Also explain common architectural principles of Deep Network?

      Also asked:Mar 2024 ·Feb 2023

      [5]
    2. b)

      Short Note on LSTM networks & GRU networks

      [5]
    3. c)

      Write a Short note on Autoencoders and Restricted Boltzmann Machines

      [5]

  2. Q2

    1. a)

      Enlist and Explain any five popular industrial tools used for Deep Learning

      Also asked:Mar 2024 ·Feb 2023

      [5]
    2. b)

      Explain Bias-Variance Tradeoff.

      Also asked:Mar 2024

      [5]
    3. c)

      Explain Convolution Neural Networks with Diagram.

      Also asked:May 2023

      [5]

  3. Q3

    1. a)

      Enlist Activation functions used in Deep Neural Network. Explain any two of them in detail.

      Also asked:Feb 2023

      [5]
    2. b)

      What are the methods for tuning hyperparameters.

      [5]
    3. c)

      What is the Loss function? Enlist all and Explain any two Loss function.

      [5]

  4. Q4

    1. a)

      What is the Biological Neuron & Perceptron? What are the steps involved for training a Perceptron in Deep Learning?

      Also asked:Feb 2023

      [5]
    2. b)

      Explain how a Neural Networks can be trained with Back propagation and Forward propagation methods.

      Also asked:Feb 2023

      [5]
    3. c)

      Explain Gradient Descent. Why does the vanishing or exploding gradients problem happen? 

      [5]