B.TechSemester 72024-25Deep LearningKOT076
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Deep Learning (KOT076) - AKTU Question Paper 2024-25

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Deep Learning Previous Year Question Paper for B.Tech Semester 7, academic session 2024-25. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 7
Session:2024-25
University:AKTU / UPTU

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Questions Asked in 2024-25

Deep Learning (KOT076) — extracted question text · 100 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 10 = 20
  • a
    What is a loss function, and why is it essential for training a neural network?
  • b
    What is stochastic gradient descent?
  • c
    Explain the significance of the “AI Winter” in deep learning history
  • d
    What is the probabilistic theory of deep learning?
  • e
    Define Linear Discriminant Analysis (LDA)
  • f
    Explain how distance metrics are used in machine learning
  • g
    Define non-convex optimization
  • h
    What is generalization in deep learning? 4 K1 i. What is WaveNet, and why is it significant for audio generation? 5 K1 j. What is Word2Vec?
Section BAttempt any three of the following: 10 x 3 = 30
  • a
    Illustrate how activation functions like ReLU and Sigmoid work in a shallow network
  • b
    Explain the role of the generator and discriminator in GANs
  • c
    What is the significance of the AlexNet architecture in deep learning history?
  • d
    Explain the role of STNs in addressing spatial invariance in deep learning
  • e
    Describe the role of deep learning in bioinformatics applications
Section CAttempt any one part of the following: 10 x 1 = 10
  • a
    Explain the purpose of backpropagation in training neural networks
  • b
    Describe the role of stochastic gradient descent (SGD) in optimization
  • a
    Demonstrate the working of a CNN on image classification tasks
  • b
    Demonstrate how batch normalization reduces internal covariate shift
  • a
    Demonstrate the effect of Xavier and He initialization on training a ConvNet
  • b
    Illustrate the role of residual connections in ResNet and their importance
  • a
    Explain how LSTMs address the vanishing gradient problem in RNNs
  • b
    Explain how RNNs are used for next-word prediction in text data
  • a
    Describe how attention mechanisms improve the performance of im age captioning models
  • b
    Explain how semantic segmentation helps in scene understanding

Question text is extracted from the official AKTU question paper PDF above. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.

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