Question: Q 2 4 Part 2 : Classification ( 1 4 points ) 1 4 Points Instructions for Question 2 4 : Subsections 2 4 .

Q24 Part 2: Classification (14 points)
14 Points
Instructions for Question 24:
Subsections 24.1 and 24.3: The answer that you input for the values in the weight vector should be real with one digit after the decimal point (e.g.[1.0,1.1,0.9,0.9,-0.9]). Note that there are no spaces between numbers. Use commas to separate numbers. You may include any relevant work for potential partial credit into the PDF you upload for subsection 24.2.
Subsection 24.2: You will upload a single PDF file here. In this PDF, please include your answer to subsection 24.2 along with any supporting work for subsections 24.1 and 24.3. Your work may be used to assign partial credit in case your answers to 24.1 and 24.3 are wrong, but showing work is not required.
Suppose we have a dataset of three sentences:
"good movie", +
"great film", +
"bad movie", -
Suppose our bag-of-words vocabulary is five tokens: \{good, movie, great, film, bad\}.
Q24.1
5 Points
Suppose we initialize the weight vector to \([1.0,1.0,1.0,1.0,1.0]\) and execute perceptron with \(\alpha=\)0.1. Give the new weight vector from executing one epoch of perceptron.
Q24.2
5 Points
Now suppose we modify perceptron to use the following loss:
\(\mathcal{L}(w,\mathbf{x}, y)=0\) if correct
\(\mathcal{L}(w,\mathbf{x}, y)=\left(w^{\top} f(\mathbf{x})\right)^{2}\) else
Write the correct update for this loss.
Please upload your answer for subsection 24.2 along with any relevant supporting work for subsections 24.1 and 24.3 in a single pdf file here:
Please select file(s)
Q 2 4 Part 2 : Classification ( 1 4 points ) 1 4

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