What is the primary focus of the initial section of the video?
Predictive Analytics with TensorFlow 6.5: Using Word2vec for Sentiment Analysis

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Information Technology (IT), Architecture
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explaining the TF-IDF model
Introducing the CBoW model for word embedding
Discussing the architecture of neural networks
Describing the Cornell University movie review dataset
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the first step in preparing the movie review dataset?
Splitting the data into training and test sets
Building a dictionary of words
Normalizing the text
Downloading and preprocessing the data
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which function is used to convert text data into numerical data?
load_movie_data
text_to_numbers
normalize_text
build_dictionary
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the noise contrastive estimation loss in the CBoW model?
To initialize word embeddings
To split the dataset into training and test sets
To handle sparse categorical output
To preprocess the movie review data
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which optimizer is used in the training of the CBoW model?
Adam optimizer
RMSProp optimizer
Gradient descent optimizer
Stochastic gradient descent
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the next step after training the CBoW model?
Evaluating the model using a test set
Building a dictionary of words
Saving the trained model for future use
Normalizing the text data
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the performance of the logistic regression model evaluated?
By splitting the dataset into training and test sets
By building a dictionary of words
By normalizing the text data
By checking the accuracy of predictions
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