Deep learning, Neural Networks, CNN RNN, LSTM

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About the Quiz

Quiz will ask 20 randomly selected questions with allotted time of . You can take the quiz more than once. Once you submit the quiz, you can review how you have done, the correct the answers for each questions and the explanation for the correct the answer.

Quiz Topics

4 Modules

Long Short-Term Memory Networks (LSTM)

6 topics
1.

Advantages of LSTM over RNN

10 questions
2.

Applications of LSTM (e.g., Text Generation, Speech Recognition)

10 questions
3.

Gates in LSTM (Input, Forget, Output)

10 questions
4.

LSTM Architecture

10 questions
5.

Sequence-to-Sequence Models

10 questions
6.

Training LSTM Networks

10 questions

Recurrent Neural Networks (RNN)

6 topics

Convolutional Neural Networks (CNN)

7 topics

Neural Networks

8 topics
Sample questions

Which of the following statements about activation functions in neural networks is true?

Activation functions introduce non-linearity into the model.

ReLU can suffer from the vanishing gradient problem.

Sigmoid activation functions are preferred for hidden layers in deep networks.

Softmax is typically used in the output layer for multi-class classification.

In the context of Convolutional Neural Networks (CNNs), what is the purpose of pooling layers?

To reduce the spatial dimensions of the input volume.

To increase the number of parameters in the model.

To introduce non-linearity into the model.

To extract features from the input data.

Which of the following optimizers is known for adapting the learning rate during training?

SGD (Stochastic Gradient Descent)

Adam

RMSprop

Adagrad

What is the main advantage of using LSTM (Long Short-Term Memory) networks over traditional RNNs (Recurrent Neural Networks)?

LSTMs can remember information for long periods.

LSTMs are simpler to implement than RNNs.

LSTMs do not suffer from the vanishing gradient problem.

LSTMs can process data in parallel.

Which of the following techniques can help prevent overfitting in neural networks?

Dropout

Batch normalization

Increasing the number of epochs

Data augmentation

Quiz Topics

4 Modules

Long Short-Term Memory Networks (LSTM)

6 topics
1.

Advantages of LSTM over RNN

10 questions
2.

Applications of LSTM (e.g., Text Generation, Speech Recognition)

10 questions
3.

Gates in LSTM (Input, Forget, Output)

10 questions
4.

LSTM Architecture

10 questions
5.

Sequence-to-Sequence Models

10 questions
6.

Training LSTM Networks

10 questions

Recurrent Neural Networks (RNN)

6 topics

Convolutional Neural Networks (CNN)

7 topics

Neural Networks

8 topics