Deep Learning: UNIT 3: RNN

 UNIT III

RNN

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1.     Handling Branches
2.     Layers
3.     Nodes
4.     Essential Elements
5.     Vanilla RNNs
6.     GRUs

Deep Learning: UNIT 3 : RNN : Short Answer Questions

                                                                              UNIT 3

RNN

Short Answer Questions

1.     List the different types of Recurrent Neural Networks.

2.     List the different variants in of RNNs with RNN Nodes.

3.     What is the purpose of RNN?

4.     List the different applications of RNNs.

5.     Define RNN Layer.

6.     Define RNN Node.

7.     List the essential elements of RNN.

8.     How can you handle branches in RNN?

Deep Learning: UNIT- 3: RNN: Long Answer Questions

                                                                             UNIT 3

RNN

Long Answer Questions

1.     Explain RNNLayer with neat diagram.

2.     Explain RNNNode with example.

3.     What is the drawback of Artificial Neural Network? How can you overcome that? Explain.

4.     Explain how RNNs handle sequential data.

5.     Explain the basic three structures of RNN

6.     What is the role of the hidden state in an RNN?

7.     What is a Recurrent Neural Network (RNN) and how does it differ from a feedforward neural network?

8.     Draw and explain the architecture of Recurrent Neural Networks.

9.     Draw and explain Schematic diagram of a recurrent neural network?

10.  List the different types of Recurrent Neural Networks. Explain them with example.

11.  Explain the essential elements of RNNNodes.

 

Deep Learning: UNIT 3 (A): RNN: Notes

 UNIT III (A)

RNN

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1.     Handling Branches

2.     Layers

3.     Nodes

4.     Essential Elements


Deep Learning: UNIT 3 (A) : RNN : PPTs


UNIT III (A)

RNN

1.     Handling Branches

2.     Layers

3.     Nodes

4.     Essential Elements


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-1 : Deep Learning Fundamentals- Short Answer Questions

 UNIT-1

Deep Learning: Fundamentals

Short Answer Questions

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1.     Define Artificial Neural Network.


2.     Define Neuron.


3.     List the operations performed by ANN layers.


4.     List the different applications of Deep Learning.


5.     Define Deep Learning.


6.     List the different applications of Artificial Neural Network.


7.     List the Building Block of Neural Networks.


8.     Define Dense layer.


9.     What is loss function.


10.  Identify the different layers in ANN.


11.  Explain Forward Pass.


12.  Explain Backward Pass.

13.  List the different optimizers.

14.  How to overcome vanishing and exploding gradient problems

15.  List the difficulties in convergence.

16.  Define Preprocessing.

17.  Define Momentum.

18.  What is Learning Rate Decay?

19.  What is the purpose of weight initialization?

20.  What is Regularization?

21.  List different Regularization techniques.

22.  Define Dropout.

23.  Define SoftMax activation function.

24.  When we use cross entropy loss function?

25.  List the different activation functions.

26.  Define sigmoid activation function.

27.  Define tanh activation function.

28.  Define ReLU activation function.

29.  How to train the neural network?


30.  Compare the ReLU activation function with the sigmoid activation function.

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