## In this blog you will find the correct answer of the Coursera quiz Introduction to Machine Coursera Week 4 Quiz mixsaver always try to brings best blogs and best coupon codes

Week 4 Comprehensive

1.
Question 1
What is meant by “word vector”?

1 point

• The latitude and longitude of the place a word originated.
• A vector of numbers associated with a word.
• Assigning a corresponding number to each word.
• A vector consisting of all words in a vocabulary.

2.
Question 2
Which word is a synonym for “word vector”?

1 point

• Norm
• Array
• Embedding
• Stack

3.
Question 3
What is the term for a set of vectors, with one vector for each word in the vocabulary?

1 point

• Space
• Array
• Codebook
• Embedding

4.
Question 4
What is natural language processing?

1 point

• Making natural text conform to formal language standards.
• Translating natural text characters to unicode representations.
• Translating human-readable code to machine-readable instructions.
• Taking natural text and making inferences and predictions.

5.
Question 5
What is the goal of learning word vectors?

1 point

• Find the hidden or latent features in a text.
• Labelling a text corpus, so a human doesn’t have to do it.
• Determine the vocabulary in the codebook.
• Given a word, predict which words are in its vicinity.

6.
Question 6
What function is the generalization of the logistic function to multiple dimensions?

1 point

• Hyperbolic tangent function
• Exponential log likelihood
• Squash function
• Softmax function

7.
Question 7
What is the continuous bag of words (CBOW) approach?

1 point

• Vectors for the neighborhood of words are averaged and used to predict word n.
• Word n is used to predict the words in the neighborhood of word n.
• Word n is learned from a large corpus of words, which a human has labeled.
• The code for word n is fed through a CNN and categorized with a softmax.

8.
Question 8
What is the Skip-Gram approach?

1 point

• Word n is used to predict the words in the neighborhood of word n.
• The code for word n is fed through a CNN and categorized with a softmax.
• Word n is learned from a large corpus of words, which a human has labeled.
• Vectors for the neighborhood of words are averaged and used to predict word n.

9.
Question 9
What is the goal of the recurrent neural network?

1 point

• Learn a series of images that form a video.
• Predict words more efficiently than Skip-Gram.
• Synthesize a sequence of words.
• Classify an unlabeled image.

10.
Question 10
Which model is the state-of-the-art for text synthesis?

1 point

• Long short-term memory
• CNN
• Multilayer perceptron
• CBOW