NPTEL Introduction To Machine Learning Week 5 Assignment Solutions

## NPTEL Introduction To Machine Learning Week 5 Assignment Answer 2023

**1. The perceptron learning algorithm is primarily designed for:**

- Regression tasks
- Unsupervised learning
- Clustering tasks
- Linearly separable classification tasks
- Non-linear classification tasks

**Answer :- For Answer ****Click Here**

**2. The last layer of ANN is linear for and softmax for .**

- Regression, Regression
- Classification, Classification
- Regression, Classification
- Classification, Regression

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**3. Consider the following statement and answer True/False with corresponding reason:**

The class outputs of a classification problem with a ANN cannot be treated independently.

- True. Due to cross-entropy loss function
- True. Due to softmax activation
- False. This is the case for regression with single output
- False. This is the case for regression with multiple outputs

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**4. Given below is a simple ANN with 2 inputs X1,X2∈{0,1} and edge weights −3,+2,+2**

**Which of the following logical functions does it compute?**

- XOR
- NOR
- NAND
- AND

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**5. Using the notations used in class, evaluate the value of the neural network with a 3-3-1 architecture (2-dimensional input with 1 node for the bias term in both the layers). The parameters are as follows**

Using sigmoid function as the activation functions at both the layers, the output of the network for an input of (0.8, 0.7) will be (up to 4 decimal places)

- 0.7275
- 0.0217
- 0.2958
- 0.8213
- 0.7291
- 0.8414
- 0.1760
- 0.7552
- 0.9442
- None of these

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**6. If the step size in gradient descent is too large, what can happen?**

- Overfitting
- The model will not converge
- We can reach maxima instead of minima
- None of the above

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**7. On different initializations of your neural network, you get significantly different values of loss. What could be the reason for this?**

- Overfitting
- Some problem in the architecture
- Incorrect activation function
- Multiple local minima

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**8. The likelihood L(θ|X) is given by:**

- P(θ|X)
- P(X|θ)
- P(X).P(θ)
- P(θ)P(X)

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**9. Why is proper initialization of neural network weights important?**

- To ensure faster convergence during training
- To prevent overfitting
- To increase the model’s capacity
- Initialization doesn’t significantly affect network performance
- To minimize the number of layers in the network

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**10. Which of these are limitations of the backpropagation algorithm?**

- It requires error function to be differentiable
- It requires activation function to be differentiable
- The ith layer cannot be updated before the update of layer i+1 is complete
- All of the above
- (a) and (b) only
- None of these

**Answer :- **For Answer **Click Here**

Course Name | Introduction To Machine Learning |

Category | NPTEL Assignment Answer |

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