NPTEL Deep Learning – IIT Ropar Week 8 Assignment Solutions

## NPTEL Deep Learning – IIT Ropar Week 8 Assignment Answer 2023

**1. Which of the following best describes the concept of saturation in deep learning?**

- When the activation function output approaches either 0 or 1 and the gradient is close to zero.
- When the activation function output is very small and the gradient is close to zero.
- When the activation function output is very large and the gradient is close to zero.
- None of the above.

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**2. Which of the following methods can help to avoid saturation in deep learning?**

- Using a different activation function.
- Increasing the learning rate.
- Increasing the model complexity
- All of the above.

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**3. Which of the following is true about the role of unsupervised pre-training in deep learning?**

- It is used to replace the need for labeled data
- It is used to initialize the weights of a deep neural network
- It is used to fine-tune a pre-trained model
- It is only useful for small datasets

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**4. Which of the following is an advantage of unsupervised pre-training in deep learning?**

- It helps in reducing overfitting
- Pre-trained models converge faster
- It improves the accuracy of the model
- It requires fewer computational resources

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**5. What is the main cause of the Dead ReLU problem in deep learning?**

- High variance
- High negative bias
- Overfitting
- Underfitting

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**6. How can you tell if your network is suffering from the Dead ReLU problem?**

- The loss function is not decreasing during training
- The accuracy of the network is not improving
- A large number of neurons have zero output
- The network is overfitting to the training data

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**7. What is the mathematical expression for the ReLU activation function?**

- f(x) = x if x < 0, 0 otherwise
- f(x) = 0 if x > 0, x otherwise
- f(x) = max(0,x)
- f(x) = min(0,x)

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**8. What is the main cause of the symmetry breaking problem in deep learning?**

- High variance
- High bias
- Overfitting
- Equal initialization of weights

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**9. What is the purpose of Batch Normalization in Deep Learning?**

- To improve the generalization of the model
- To reduce overfitting
- To reduce bias in the model
- To ensure that the distribution of the inputs at different layers doesn’t change

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**10. In Batch Normalization, which parameter is learned during training?**

- Mean
- Variance
- γ
- ϵ

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Course Name | Deep Learning – IIT Ropar |

Category | NPTEL Assignment Answer |

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