
ML B2 CH8
Quiz
•
Computers
•
University
•
Practice Problem
•
Medium
Jhonston Benjumea
Used 1+ times
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10 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main limitation of the traditional seq2seq model?
Answer explanation
Traditional seq2seq models compress all input into a fixed-length vector, which limits performance on longer inputs.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the attention mechanism allow the decoder to use?
Answer explanation
Attention allows the decoder to focus on all encoder hidden states, assigning different weights to each.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the context vector in attention calculated?
Answer explanation
The context vector is a weighted sum of all encoder output vectors based on attention weights.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which mathematical operation is used to compute attention scores?
Answer explanation
Attention scores are calculated using the dot product between decoder state and encoder outputs.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of the softmax function in attention?
Answer explanation
Softmax normalizes raw attention scores into a probability distribution over encoder outputs.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does a Time Attention layer do?
Answer explanation
Time Attention layers apply attention mechanisms at each time step of the sequence.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is bidirectional RNN used for in attention-based models?
Answer explanation
Bidirectional RNNs allow the model to gather information from both past and future inputs.
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