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We have one-hot encoded our sentence into a representation that a neural network could digest. Word-level encoding can be done the same way by establishing a vocabu- lary and one-hot encoding sentences-sequences of words-along the rows of our tensor. Since a vocabulary has many words, this will produce very wide encoded vec- tors, which may not be practical. We will see in the next section that there is a more efficient way to represent text at the word level, using embeddings. For now, let's stick with one-hot encodings and see what happens. We'll define clean_words, which takes text and returns it in lowercase and stripped of punctuation. When we call it on our "Impossible, Mr. Bennet" line, we get the following:

We have one-hot encoded our sentence into a representation that a neural network could digest. Word-level encoding can be done the same way by establishing a vocabu- lary and one-hot encoding sentences-sequences of words-along the rows of our tensor. Since a vocabulary has many words, this will produce very wide encoded vec- tors, which may not be practical. We will see in the next section that there is a more efficient way to represent text at the word level, using embeddings. For now, let's stick with one-hot encodings and see what happens. We'll define clean_words, which takes text and returns it in lowercase and stripped of punctuation. When we call it on our "Impossible, Mr. Bennet" line, we get the following:

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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We have one-hot encoded our sentence into a representation that a neural network could digest. Word-level encoding can be done the same way by establishing a vocabu- lary and one-hot encoding sentences-sequences of words-along the rows of our tensor. Since a vocabulary has many words, this will produce very wide encoded vec- tors, which may not be practical. We will see in the next section that there is a more efficient way to represent text at the word level, using embeddings. For now, let's stick with one-hot encodings and see what happens. We'll define clean_words, which takes text and returns it in lowercase and stripped of punctuation. When we call it on our "Impossible, Mr. Bennet" line, we get the following:
Transcribed Image Text:We have one-hot encoded our sentence into a representation that a neural network could digest. Word-level encoding can be done the same way by establishing a vocabu- lary and one-hot encoding sentences-sequences of words-along the rows of our tensor. Since a vocabulary has many words, this will produce very wide encoded vec- tors, which may not be practical. We will see in the next section that there is a more efficient way to represent text at the word level, using embeddings. For now, let's stick with one-hot encodings and see what happens. We'll define clean_words, which takes text and returns it in lowercase and stripped of punctuation. When we call it on our "Impossible, Mr. Bennet" line, we get the following:
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