AKTU · B.Tech · Important questions
AKTU Deep Learning Important Questions
The questions AKTU asks again and again in Deep Learning, found by comparing every question in 5 previous year papers from 2022-23 to 2025-26 (subject codes BAI701, KCS078, KDS078, KOT076).
How this list is made: we compared all 122 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Explain the concept of semi-supervised learning with examples. Compare it with supervised and unsupervised learning.
- Examine the Semi –Supervised learning.
- How does semi-supervised learning differ from supervised and 2 K 1 , unsupervised learning? Discuss its application in training deep networks K with limited labeled data.
- Explain the difference between Gradient Descent and Stochastic Gradient Descent.
- Describe the role of stochastic gradient descent (SGD) in optimization.
- Discuss how batch gradient descent and stochastic gradient descent are different.
- Explain the concept of stochastic gradient descent (SGD). How does it differ from batch and mini-batch gradient descent in terms of computational efficiency and convergence?
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Analyze the challenges of stochastic optimization in deep learning. 4 K
- How do techniques address the challenges of stochastic optimization in 4 K deep learning?
- Define non-convex optimization.
- Discuss the role of non-convex optimization in deep learning. 4 K
- Define the different layers of a convolutional neural network.
- Discuss the role of convolutional layers in Convolutional Neural Networks.
- Discuss the differences between a perceptron and a support vector machine in 1 K terms of learning.
- Discuss the differences between a perceptron and a support vector 1 K 1 , machine in terms of decision boundary formation. K
- What is WaveNet, and why is it significant for audio generation?
- Describe the architecture and training of WaveNet for audio generation.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BAI701, 2024-25 KDS078, 2024-25 KCS078), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain the backpropagation algorithm in detail. Derive the weight update rule for a multilayer perceptron. (2025-26, BAI701)
- Discuss the significance of activation functions in the Universal Approximation Theorem. (2025-26, BAI701)
- Prove that neural networks can act as universal function approximators. 1 K 1 , Discuss any constraints or assumptions involved in this theorem. K (2024-25, KCS078)
- Discuss the relationship between the choice of activation function in a 1 K 1 , shallow neural network and its ability to model non-linear relationships. (2024-25, KCS078)
- Explain the concept of stochastic gradient descent (SGD). How does it differ from batch and mini-batch gradient descent in terms of computational efficiency and convergence? (2024-25, KDS078)
- Compare and contrast the theoretical underpinnings of perceptrons and logistic regression in the context of binary classification problems. (2024-25, KDS078)
Unit 2
- Compare batch normalization with layer normalization and instance normalization. (2025-26, BAI701)
- Explain the concept of semi-supervised learning with examples. Compare it with supervised and unsupervised learning. (2025-26, BAI701)
- Analyze the trade-offs between increasing network depth and the risk of 2 K 1 , vanishing/exploding gradients. What role does skip connections in ResNet play in addressing these issues? (2024-25, KCS078)
- How does semi-supervised learning differ from supervised and 2 K 1 , unsupervised learning? Discuss its application in training deep networks K with limited labeled data. (2024-25, KCS078)
- Explain the architecture and training process of a Generative Adversarial 2 K Network. What challenges arise during training, and how can they be mitigated? (2024-25, KDS078)
- Discuss the probabilistic theory behind deep learning models. How do 2 K Bayesian principles apply to the optimization and uncertainty estimation in deep networks? (2024-25, KDS078)
Unit 3
- Explain convolution operation mathematically, including stride, padding, and feature maps. (2025-26, BAI701)
- Compare AlexNet, VGG, Inception, and ResNet in terms of depth, performance, 3 and computation. (2025-26, BAI701)
- Explain the concept of metric learning in the context of dimensionality reduction. How does it differ from traditional techniques like PCA (2024-25, KCS078)
- Analyze the architectural differences between AlexNet, VGG, Inception, and ResNet. How do these architectures address the vanishing gradient problem? (2024-25, KCS078)
- Compare Principal Component Analysis and Linear Discriminant Analysis terms of their objectives and mathematical formulations. Provide examples of their use in classification tasks. (2024-25, KDS078)
- Derive the reconstruction loss of an autoencoder. How does this loss relate to the dimensionality reduction capabilities of the network? (2024-25, KDS078)
Unit 4
- Compare RNN, LSTM, and GRU networks. Highlight their advantages and use- cases. (2025-26, BAI701)
- What is deep reinforcement learning? Explain its architecture involving policy networks, value networks, and environment interaction. (2025-26, BAI701)
- Discuss the implications of non-convex optimization in designing deep learning models. How do saddle points influence the training process? K (2024-25, KCS078)
- Provide a detailed overview of computational neuroscience principles applied in designing artificial neural networks. (2024-25, KCS078)
- Explain how deep reinforcement learning combines reinforcement learning principles with deep network architectures. (2024-25, KDS078)
- Derive the architecture of a recurrent neural network language model. Discuss its strengths and limitations compared to word-level RNNs. (2024-25, KDS078)
Unit 5
- Explain how WaveNet differs from RNNs for sequential audio generation. (2025-26, BAI701)
- Explain the Word2Vec model. Discuss CBOW and Skip-Gram architectures with diagrams. (2025-26, BAI701)
- Provide a detailed overview of deep learning-based techniques for audio detection. How does WaveNet outperform traditional methods? (2024-25, KCS078)
- Describe the process of joint detection and captioning in image captioning systems. How does attention enhance this process? (2024-25, KCS078)
- Discuss the limitations of deep learning models in real-world applications. How 5 K can interpretability and ethical considerations be integrated into these systems? (2024-25, KDS078)
- Explain the architecture of a deep learning model used for face recognition. 5 K How does it ensure robustness against variations in pose and illumination? (2024-25, KDS078)
Papers used for this list
- AKTU Deep Learning (BAI701) question paper 2025-26
- AKTU Deep Learning (KCS078) question paper 2024-25
- AKTU Deep Learning (KDS078) question paper 2024-25
- AKTU Deep Learning (KOT076) question paper 2024-25
- AKTU Deep Learning (KCS078) question paper 2022-23
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Deep Learning questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 122 questions from 5 AKTU Deep Learning papers (2022-23 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Deep Learning previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
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