10 Questions
Which type of neural networks are prerequisites for understanding the sequence 2 sequence model?
Recurrent Neural Networks (RNN)
What is the purpose of attention mechanisms in the sequence 2 sequence model?
To focus on a particular area of interest
In which scenario would the level of attention be higher according to the text?
Preparing for a test
What type of architecture do Sequence to Sequence (Seq2Seq) models use?
Encoder-decoder architecture
What is one use case for Seq2Seq models?
Text summarization
Which equation represents the computation of the attention score $e$ in the sequence to sequence model?
$e = f(s_{i-1}, h_j)$
What does the context vector $c$ represent in the sequence to sequence model?
A weighted sum of the encoder hidden states
What does the alignment model $f$ in the sequence to sequence model score?
How well the inputs around position $j$ and the output at position $i$ match
How is the attention score $e$ used to compute the attention weights $\alpha_{ij}$?
By taking a softmax over the attention scores $e$ with respect to the $i$th output
How does attention help alleviate the vanishing gradient problem in the sequence to sequence model?
By providing a direct path to the inputs
Test your knowledge on the Sequence 2 Sequence model with Attention Mechanism! This quiz will cover the prerequisites, such as LSTM and GRU, as well as the concept of attention in neural machine translation. Challenge yourself with scenario-based questions to understand the importance of attention mechanisms in the sequence 2 sequence model.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
