sequence model coursera week 2 quiz answers

• True
• False

2. What is t-SNE?

• A supervised learning algorithm for learning word embeddings
• A linear transformation that allows us to solve analogies on word vectors
• A non-linear dimensionality reduction technique
• An open-source sequence modeling library

• False
• True

5. Let A be an embedding matrix, and let 04567 be a one-hot vector corresponding to word 4567. Then to get the embedding of word 4567, why don't we call A * 04567 in Python?

• It is computationally wasteful.
• The correct formula is AT * 04567
• None of the answers are correct: calling the Python snippet as described above is fine.
• This doesn’t handle unknown words (<UNK>).

• True
• False

7. In the word2vec algorithm, you estimate P(t | c), where t is the target word and c is a context word. How are t and c chosen from the training set? Pick the best answer.

• c is the one word that comes immediately before t
• c and t are chosen to be nearby words.
• c is the sequence of all the words in the sentence before t
• c is a sequence of several words immediately before t

• False
• True

• True
• False

• m1 << m2
• m1 >> m2