Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: Vectorizer

The video tutorial covers the process of vectorizing text reviews using one-hot encoding and building a vectorizer function in Python. It then tests the function with sample reviews to ensure it outputs the correct feature vectors. The tutorial concludes with an overview of a recurrent neural network (RNN) architecture, highlighting the use of tanh and sigmoid activations, and explaining how the model processes input sequences to produce output only for the last word in a review.

What is the primary purpose of the vectorizer function introduced in the video?

• To convert reviews into numerical feature vectors

• To generate random reviews

• To summarize reviews

• To translate reviews into different languages

Which encoding technique is used for vectorizing words in the reviews?

• Ordinal encoding

• One-hot encoding

• Binary encoding

• Frequency encoding

What is the role of the 'NP dot H stack' function in the vectorizer?

• To split the review into words

• To stack feature vectors horizontally

• To remove punctuation from the review

• To initialize the one-hot vectors

What does the shape of the output from the vectorizer function represent?

• The number of sentences in the review

• The length of the vocabulary and the number of words in the review

• The number of reviews processed

• The average word length in the review

What activation function is used in the RNN cell described in the video?

• Softmax

• Tanh

• ReLU

• Sigmoid

In the RNN architecture, when is the output value considered for comparison with the ground truth?

• After every sentence

• Only after the last word

• Only after the first word

• After every word

What happens to the output values from previous unrolls in the RNN?

• They are averaged

• They are discarded

• They are used for backpropagation

• They are stored for future use