Deep Learning - Recurrent Neural Networks with TensorFlow - Text Preprocessing

The video tutorial covers text preprocessing using a notebook. It starts with importing necessary classes and functions, creating a dummy dataset, and setting a maximum vocabulary size. The process of tokenizing sentences into sequences of integers is explained, along with the concept of padding sequences to ensure uniform length. Different padding and truncating techniques are demonstrated, highlighting their effects on sequence data.

What is the purpose of setting a maximum vocabulary size in text preprocessing?

• To ensure all words are included

• To reduce the size of the output file

• To limit the number of unique words processed

• To increase the processing speed

How does the Tokenizer class transform text data?

• By removing punctuation from text

• By converting text into sequences of integers

• By splitting text into individual characters

• By converting text into lowercase

What information does the tokenizer's word index provide?

• The mapping of words to their corresponding integers

• The length of each sentence

• The frequency of each word

• The number of sentences in the dataset

What is the default behavior of the pad_sequences function?

• It pads sequences to the length of the longest sentence

• It truncates sequences to the shortest sentence

• It removes all padding from sequences

• It adds padding to the end of each sequence

How does setting padding to 'post' affect the sequences?

• It adds padding at the beginning of sequences

• It doubles the length of sequences

• It adds padding at the end of sequences

• It removes padding from sequences

What happens when the max length is set to a number less than the maximum sequence length?

• Sequences are padded to the new length

• Sequences are duplicated

• Sequences are truncated from the beginning

• Sequences are left unchanged

When truncating sequences, what does setting the truncating argument to 'post' do?

• It adds padding to sequences

• It truncates the end of sequences

• It removes all sequences

• It truncates the beginning of sequences