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Q1: Describe the significance of the ResNet architecture in deep learning. How does it
address the vanishing gradient problem?
Q2: How do CNNs contribute to object detection frameworks like Faster R-CNN and
YOLO?
Q3: Explain how transfer learning is applied in CNNs for image classification tasks.
What are the benefits?
Q4: How does the depth of a CNN affect its ability to extract features, and what
challenges arise with increasing depth?
Q5: Describe the role of CNNs in Fully Convolutional Networks (FCNs) for image
segmentation. How do FCNs differ from traditional CNNs used in image classification?
Q6: Discuss the challenges associated with training RNNs, particularly focusing on the
issues of vanishing and exploding gradients. How can these issues be addressed?
Q7: Explain the architecture of LSTM networks and how they differ from basic RNNs in
handling long-term dependencies.
Q8: Describe the concept and applications of Bi-directional RNNs. How do they
improve upon standard RNNs?
Q9: Explain the basic architecture of a GAN and describe the training dynamics
between the generator and discriminator. How does the adversarial process
contribute to the learning of both networks?
Q10: Describe the concept of Conditional GANs (cGANs) and their advantages over
traditional GANs.
Q11: How can GANs be utilized for data augmentation, and what are the potential
benefits and drawbacks of this approach?
Q12: How can GANs be utilized for data augmentation, and what are the potential
benefits and drawbacks of this approach?
Q13: How are GANs applied in unsupervised learning scenarios, and what
advantages do they offer over traditional unsupervised learning methods?
Q14: Discuss the challenges and considerations in multi-agent reinforcement learning
compared to single-agent environments.
Q15: Provide examples of how reinforcement learning is applied in real-world
scenarios. What are the practical challenges in deploying RL models?
Q16: Describe the architecture of transformer-based LLMs and the significance of the
pre-training process. How do these models achieve their capabilities?
Q17: Explain the concept of fine-tuning in the context of LLMs and its importance for
transfer learning. What makes LLMs particularly suited for transfer learning?
Q18: Discuss the ethical considerations and the challenge of bias in LLMs. How can
these issues be addressed?
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