NPTEL Introduction To Machine Learning Week 5 Assignment Solutions
![NPTEL Introduction To Machine Learning Week 5 Assignment Answer 2023 2 Week 1 NPTEL Introduction To Machine Learning Assignment Answer 2023](https://dbcitanagar.com/wp-content/uploads/Introduction-To-Machine-Learning-1024x576.png)
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
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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
![NPTEL Introduction To Machine Learning Week 5 Assignment Answer 2023 3 image 49](https://gecmunger.in/wp-content/uploads/2023/08/image-49.png)
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
![NPTEL Introduction To Machine Learning Week 5 Assignment Answer 2023 4 image 50](https://gecmunger.in/wp-content/uploads/2023/08/image-50.png)
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
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Course Name | Introduction To Machine Learning |
Category | NPTEL Assignment Answer |
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