Deep Learning - Convolutional 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 word-index mapping. The tutorial then demonstrates how to use the pad sequences function with default and custom settings, and explores truncation options for sequences, highlighting the impact on sequence length and padding.

What is the primary purpose of importing the Tokenizer class and pad_sequences function?

• To generate random numbers

• To perform mathematical calculations

• To preprocess text data

• To create a graphical user interface

Why is a vocabulary size of 20,000 considered reasonable?

• It is the default size in TensorFlow

• It covers all words in the Oxford Dictionary

• It covers about 95% of most texts

• It is the maximum size allowed by most libraries

What is the role of the tokenizer's fit_on_texts method?

• To calculate the frequency of words

• To map words to integers

• To convert sequences back to text

• To remove stop words from text

How can you determine which word corresponds to a specific integer in the tokenized sequences?

• By using the tokenizer's word_index attribute

• By checking the original text

• By guessing based on context

• By using a separate dictionary

What happens when you set padding to 'post' in pad_sequences?

• Sequences are left unchanged

• Sequences are truncated from the end

• Padding is added at the end of sequences

• Padding is added at the beginning of sequences

If you set maxlen to a value less than the maximum sequence length, what is the default truncation behavior?

• Truncate from the end

• Add padding to the end

• Truncate from the beginning

• Add padding to the beginning

What is the effect of setting the truncating argument to 'post' when maxlen is less than the sequence length?

• The beginning of the sequence is truncated

• The end of the sequence is truncated

• Padding is added to the beginning

• Padding is added to the end