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Questions and Answers
Which type of neural networks are prerequisites for understanding the sequence 2 sequence model?
Which type of neural networks are prerequisites for understanding the sequence 2 sequence model?
- Recurrent Neural Networks (RNN) (correct)
- Convolutional Neural Networks (CNN)
- Feedforward Neural Networks (FNN)
- Generative Adversarial Networks (GAN)
What is the purpose of attention mechanisms in the sequence 2 sequence model?
What is the purpose of attention mechanisms in the sequence 2 sequence model?
- To reduce the complexity of the encoder-decoder architecture
- To focus on a particular area of interest (correct)
- To improve the accuracy of neural machine translation
- To increase the efficiency of recurrent neural networks
In which scenario would the level of attention be higher according to the text?
In which scenario would the level of attention be higher according to the text?
- Reading an article related to the current news
- Both scenarios have the same level of attention
- The text does not provide enough information to determine
- Preparing for a test (correct)
What type of architecture do Sequence to Sequence (Seq2Seq) models use?
What type of architecture do Sequence to Sequence (Seq2Seq) models use?
What is one use case for Seq2Seq models?
What is one use case for Seq2Seq models?
Which equation represents the computation of the attention score $e$ in the sequence to sequence model?
Which equation represents the computation of the attention score $e$ in the sequence to sequence model?
What does the context vector $c$ represent in the sequence to sequence model?
What does the context vector $c$ represent in the sequence to sequence model?
What does the alignment model $f$ in the sequence to sequence model score?
What does the alignment model $f$ in the sequence to sequence model score?
How is the attention score $e$ used to compute the attention weights $\alpha_{ij}$?
How is the attention score $e$ used to compute the attention weights $\alpha_{ij}$?
How does attention help alleviate the vanishing gradient problem in the sequence to sequence model?
How does attention help alleviate the vanishing gradient problem in the sequence to sequence model?
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