Deep Learning: UNIT 2: CNN: Short Answer Questions

  UNIT II
CNN
Short Answer Questions

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1.     List the applications of CNN.

2.     Define Convolution.

3.     Define Stride.

4.     Explain padding.

5.     What the purpose of padding?

6.     Define Kernal.

7.     Define Pooling.

8.     List the different pooling techniques.

9.     Define Flattening.

10.  Define Fully Connected Layer.

11.  List the difference between CNN and Fully Connected Layer.

12.  What is a filter (or kernel) in the context of a CNN?

13.  Discuss the role of fully connected layers in CNNs.

14.  Explain the concept of pooling in CNNs and how it sometimes impacts output size and can cause underfitting.

15.  What is the purpose of the pooling layer in a CNN?

16.  what are two special cases of padding? explain them with a neat diagram.

17.  Discuss the concept of convolution in CNNs.

18.  How do filters (kernels) help in feature extraction, and how are these filters learned during the training process?

19.  What is the role of pooling in CNNs?

20.  How does pooling in CNNs reduce the spatial dimensions of feature maps?

21.  Discuss the trade-offs between using smaller and larger pooling windows in CNNs.

22.  How does the choice of pooling size affect the information retained in the feature maps?

23.  Compare max pooling and average pooling, and explain how pooling layers help in reducing the dimensionality of feature maps.

24.  How does pooling help to control the size of the output feature map in a Convolutional Neural Network (CNN)?

 

Deep Learning: UNIT-2 : CNN: Long Answer Questions

UNIT II

CNN

Long Answer Questions

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1.     Explain CNN with an example.

2.     List the different applications of CNN.

3.     Write an example function for Convolution and Pooling operations and explain in detail.

4.     Draw and explain the architecture of convolution neural networks.

5.     Explain about the convolutional layers in CNN.

6.     Explain striding and padding in CNN with example.

7.     Draw the structure of CNN.

8.     Apply CNN architecture to Classify MNIST Hand Written Dataset.

9.     List the difference between CNN and Fully Connected Layers.

Deep Learning: UNIT-2 CNN

 UNIT II

CNN

1.     Introduction
2.     striding and padding
3.     pooling layers
4.     structure
5.     operations and prediction of CNN with layers
6.     CNN -Case study with MNIST

7.     CNN VS Fully Connected

 👉Deep Learning: UNIT-2: CNN PPTs

👉Deep Learning: UNIT-2 CNN Notes

👉Deep Learning: UNIT-2 : CNN: Long Answer Questions

👉Deep Learning: UNIT-2: CNN : Short Answer Questions

Deep Learning: UNIT-2 PPT

UNIT II 

CNN 

1. Introduction 

2. striding and padding 

 3. pooling layers 

4. structure 

5. operations and prediction of CNN with layers 

6. CNN -Case study with MNIST 

7. CNN VS Fully Connected

Deep Learning: UNIT 2- CNN Notes

                                                                              UNIT II 

CNN 

1. Introduction 

2. striding and padding 

 3. pooling layers 

4. structure 

5. operations and prediction of CNN with layers 

6. CNN -Case study with MNIST 

7. CNN VS Fully Connected