How is the final attention output computed using the attention weights and value vectors?<br /> A. a. By taking the dot product of the attention weights and value vectors <br />B. b. By concatenating the attention weights and value vectors <br />C. c. By taking a weighted sum of the value vectors using the attention weights <br />D. d. By adding the attention weights to the value vectors element-wise

What are the two main steps of the attention mechanism?Calculating the context vector and generating the attention weightsCalculating the attention weights and generating the output wordCalculating the context vector and generating the output wordCalculating the attention weights and generating the context vector

What is the purpose of the attention weights?To generate the output word based on the input data alone.To assign weights to different parts of the input sequence, with the most important parts receiving the highest weights.To incrementally apply noise to the input data.To calculate the context vector by averaging words embedding in the context.

Attention scores in transformers are computed using the dot product of the query and key vectors.Group of answer choicesTrueFalse

Assume a perceptron:with 3 inputs (x1,x2,x3) plus a bias (x0) statically set to 1with weighted input= x0*w0+x1*w1+x2*w2+x3*w3that outputs 1 if weighted input > 0, else 0with initial weights are all set to 0with weight updating as follows: Wi j+1= Wi j+ a * (Target j- Output j) * X i and a learning rate a=1 How will the final weight vector look like when all data-items are processed? 1 0 1 1 0 0 -1 0 0 -1 0 0 1 0 1 0 None of the above

When calculating attention scores using masking, which operation is performed to mask out irrelevant elements?Question 25Answera.Addition of the mask matrix to the attention scoresb. Concatenation of the mask matrix with the attention scoresc.Division of the attention scores by the mask matrixd.Element-wise multiplication with the mask matrix