B.TechSemester 72025-26Deep LearningBAI701

Deep Learning (BAI701) - AKTU Question Paper 2025-26

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 2025-26. 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:2025-26

University:AKTU / UPTU

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Questions Asked in 2025-26

Deep Learning (BAI701) — complete question paper · 70 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 02 x 7 = 14

a
Define model in machine learning
b
What is the difference between linear regression and logistic regression?
c
Discuss convolutional neural network (CNN)?
d
Name any two activation functions used in deep networks
e
Describe the role of regularization during optimization
f
Discuss the generalization in machine learning
g
Name two applications of Word2Vec

Section BAttempt any three o f t h e f o l l o w i n g : 07 x 3 = 21

a
Derive the mathematical expression for the output of a single hidden-layer neural network. Explain the role of activation functions
b
Describe how probabilistic models help reduce overfitting. Explain with examples
c
Compare PCA and LDA. Explain how LDA maximizes class separability using scatter matrices
d
Explain the optimization problem in deep learning. Why are deep learning loss surfaces highly non-convex? Discuss with suitable diagrams
e
Describe the ImageNet dataset in detail. Explain its scale, structure, challenges, and importance in the evolution of deep learning

Section CAttempt any one p a r t o f t h e f o l l o w i n g : 07 x 1 = 07

a
Explain the backpropagation algorithm in detail. Derive the weight update rule for a multilayer perceptron
b
Discuss the significance of activation functions in the Universal Approximation Theorem
a
Compare batch normalization with layer normalization and instance normalization
b
Explain the concept of semi-supervised learning with examples. Compare it with supervised and unsupervised learning
a
Explain convolution operation mathematically, including stride, padding, and feature maps
b
Compare AlexNet, VGG, Inception, and ResNet in terms of depth, performance, and computation
a
Compare RNN, LSTM, and GRU networks. Highlight their advantages and use- cases
b
What is deep reinforcement learning? Explain its architecture involving policy networks, value networks, and environment interaction
a
Explain how WaveNet differs from RNNs for sequential audio generation
b
Explain the Word2Vec model. Discuss CBOW and Skip-Gram architectures with diagrams

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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