Explain the process of training an ANN, focusing on the role of the loss function and optimization algorithms.
Describe the concept of "memory" in RNNs. Explain how it allows RNNs to process sequential data.
Discuss the concept of time steps in RNNs. Explain How RNN processes sequential data differently than other neural networks.
Explain the role of convolutional layers in a CNN. Discuss how convolutional filters work and the concept of feature maps.
Describe the purpose of pooling layers in CNNs. Compare max pooling and average pooling, including their effects on the network’s performance.
Discuss the role of each gate in an LSTM cell. Explain how each gate contributes to the flow of information.
Provide an example of a scenario where LSTMs would be preferred over standard RNNs, and explain the reasoning.
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Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Deep Learning and Neural Network (Winter 2025, B.E. · Computer Engineering, Sem 6). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
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