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Machine Learning Techniques Unit 4 – Artificial Neural Networks and Deep Learning: important questions for AKTU

Unit 4 (Artificial Neural Networks and Deep Learning) questions that AKTU repeats most often. This unit carries about 23 marks per paper. Start with the repeated questions, then the most asked topics.

Most repeated Unit 4 questions

  • Derive the Backpropagation algorithm for training neural networks. (7 marks, 2026, Derivation of Backpropagation Algorithm) – also asked in 2023, 2024, 2025
  • Derive the Backpropagation Algorithm for Multilayer Neural Networks. (7 marks, 2026, Derivation of Backpropagation Algorithm) – also asked in 2023, 2025
  • Explain CNN architecture and different types of layers. (7 marks, 2026, Types of CNN Layers) – also asked in 2023, 2025

Most important Unit 4 topic

  • Derivation of Backpropagation Algorithm (Unit 4: Artificial Neural Networks and Deep Learning) – asked 8 times in 2021, 2023, 2024, 2025, 2026

Most asked Unit 4 topics

  • Derivation of Backpropagation Algorithm – asked 8 times
  • Types of CNN Layers – asked 6 times
  • Gradient Descent and the Delta Rule – asked 4 times

More Unit 4 previous year questions

  • Define activation function in neural networks. (2 marks, 2026, Types of CNN Layers)
  • What is backpropagation in neural networks? (2 marks, 2026, Derivation of Backpropagation Algorithm)
  • Explain perceptron and multilayer perceptron with neat diagram. (7 marks, 2026, Multilayer Perceptron)
  • Explain the architecture of Convolutional Neural Networks (CNN). (7 marks, 2026, Concept of Convolutional Neural Network)
  • Discuss applications of Deep Learning with suitable case studies. (7 marks, 2026, Introduction to Deep Learning)

Unit 4 syllabus topics

  • Perceptrons
  • Multilayer Perceptron
  • Gradient Descent and the Delta Rule
  • Multilayer Networks
  • Derivation of Backpropagation Algorithm
  • Generalization
  • Unsupervised Learning: SOM Algorithm and Its Variants
  • Introduction to Deep Learning
  • Concept of Convolutional Neural Network
  • Types of CNN Layers
  • Concept of 1D and 2D Convolution Layers
  • Training of Network