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Natural Language Processing in TensorFlow Coursera Quiz Answers

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Natural Language Processing in TensorFlow Quiz Answers

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Week 1 Quiz Answers: Natural Language Processing in TensorFlow Coursra Quiz Answers

Question 1: What is the name of the object used to tokenize sentences?

  • Tokenizer
  • WordTokenizer
  • CharacterTokenizer
  • TextTokenizer

Question 2: What is the name of the method used to tokenize a list of sentences?

  • fit_on_texts(sentences)
  • tokenize(sentences)
  • tokenize_on_text(sentences)
  • fit_to_text(sentences)

Question 3: Once you have the corpus tokenized, what’s the method used to encode a list of sentences to use those tokens?

  • texts_to_tokens(sentences)
  • text_to_sequences(sentences)
  • text_to_tokens(sentences)
  • texts_to_sequences(sentences)

Question 4: When initializing the tokenizer, how to you specify a token to use for unknown words?

  • out_of_vocab=<Token>
  • unknown_token=<Token>
  • oov_token=<Token>
  • unknown_word=<Token>

Question 5: If you don’t use a token for out of vocabulary words, what happens at encoding?

  • The word isn’t encoded, and is replaced by a zero in the sequence
  • The word is replaced by the most common token
  • The word isn’t encoded, and is skipped in the sequence
  • The word isn’t encoded, and the sequencing ends

Question 6: If you have a number of sequences of different lengths, how do you ensure that they are understood when fed into a neural network?

  • Make sure that they are all the same length using the pad_sequences method of the tokenizer
  • Use the pad_sequences object from the tensorflow.keras.preprocessing.sequence namespace
  • Specify the input layer of the Neural Network to expect different sizes with dynamic_length
  • Process them on the input layer of the Neural Netword using the pad_sequences property

Question 7: If you have a number of sequences of different length, and call pad_sequences on them, what’s the default result?

  • Nothing, they’ll remain unchanged
  • They’ll get cropped to the length of the shortest sequence
  • They’ll get padded to the length of the longest sequence by adding zeros to the beginning of shorter ones
  • They’ll get padded to the length of the longest sequence by adding zeros to the end of shorter ones

Question 8: When padding sequences, if you want the padding to be at the end of the sequence, how do you do it?

  • Call the padding method of the pad_sequences object, passing it ‘post’
  • Pass padding=’post’ to pad_sequences when initializing it
  • Call the padding method of the pad_sequences object, passing it ‘after’
  • Pass padding=’after’ to pad_sequences when initializing it

Week 2 Quiz Answers: Natural Language Processing in TensorFlow Coursra Quiz Answers

Question 1: What is the name of the TensorFlow library containing common data that you can use to train and test neural networks?

  • TensorFlow Datasets
  • TensorFlow Data
  • TensorFlow Data Libraries
  • There is no library of common data sets, you have to use your own

Question 2: How many reviews are there in the IMDB dataset and how are they split?

  • 60,000 records, 50/50 train/test split
  • 50,000 records, 80/20 train/test split
  • 60,000 records, 80/20 train/test split
  • 50,000 records, 50/50 train/test split

Question 3: How are the labels for the IMDB dataset encoded?

  • Reviews encoded as a number 1-10
  • Reviews encoded as a number 0-1
  • Reviews encoded as a boolean true/false
  • Reviews encoded as a number 1-5

Question 4: What is the purpose of the embedding dimension?

  • It is the number of dimensions required to encode every word in the corpus
  • It is the number of words to encode in the embedding
  • It is the number of dimensions for the vector representing the word encoding
  • It is the number of letters in the word, denoting the size of the encoding

Question 5: When tokenizing a corpus, what does the num_words=n parameter do?

  • It specifies the maximum number of words to be tokenized, and picks the most common ‘n’ words
  • It specifies the maximum number of words to be tokenized, and stops tokenizing when it reaches n
  • It errors out if there are more than n distinct words in the corpus
  • It specifies the maximum number of words to be tokenized, and picks the first ‘n’ words that were tokenized

Question 6: To use word embeddings in TensorFlow, in a sequential layer, what is the name of the class?

  • tf.keras.layers.Embedding
  • tf.keras.layers.WordEmbedding
  • tf.keras.layers.Word2Vector
  • tf.keras.layers.Embed

Question 7: IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?

  • Categorical crossentropy
  • Binary crossentropy
  • Adam
  • Binary Gradient descent

Question 8: When using IMDB Sub Words dataset, our results in classification were poor. Why?

  • Sequence becomes much more important when dealing with subwords, but we’re ignoring word positions
  • The sub words make no sense, so can’t be classified
  • Our neural network didn’t have enough layers
  • We didn’t train long enough

Week 3 Quiz Answers: Natural Language Processing in TensorFlow Coursra Quiz Answers

Question 1: Why does sequence make a large difference when determining semantics of language?

  • Because the order of words doesn’t matter
  • Because the order in which words appear dictate their meaning
  • It doesn’t
  • Because the order in which words appear dictate their impact on the meaning of the sentence

Question 2: How do Recurrent Neural Networks help you understand the impact of sequence on meaning?

  • They look at the whole sentence at a time
  • They shuffle the words evenly
  • They carry meaning from one cell to the next
  • They don’t

Question 3: How does an LSTM help understand meaning when words that qualify each other aren’t necessarily beside each other in a sentence?

  • They shuffle the words randomly
  • They load all words into a cell state
  • Values from earlier words can be carried to later ones via a cell state
  • They don’t

Question 4: What keras layer type allows LSTMs to look forward and backward in a sentence?

  • Bilateral
  • Unilateral
  • Bothdirection
  • Bidirectional

Question 5: What’s the output shape of a bidirectional LSTM layer with 64 units?

  • (128,1)
  • (128,None)
  • (None, 64)
  • (None, 128)

Question 6: When stacking LSTMs, how do you instruct an LSTM to feed the next one in the sequence?

  • Ensure that return_sequences is set to True only on units that feed to another LSTM
  • Ensure that return_sequences is set to True on all units
  • Do nothing, TensorFlow handles this automatically
  • Ensure that they have the same number of units

Question 7: If a sentence has 120 tokens in it, and a Conv1D with 128 filters with a Kernal size of 5 is passed over it, what’s the output shape?

  • (None, 120, 128)
  • (None, 116, 128)
  • (None, 116, 124)
  • (None, 120, 124)

Question 8: What’s the best way to avoid overfitting in NLP datasets?

  • Use LSTMs
  • Use GRUs
  • Use Conv1D
  • None of the above

Week 4 Quiz Answers: Natural Language Processing in TensorFlow Coursra Quiz Answers

Question 1: What is the name of the method used to tokenize a list of sentences?

  • tokenize(sentences)
  • tokenize_on_text(sentences)
  • fit_on_texts(sentences)
  • fit_to_text(sentences)

Question 2: If a sentence has 120 tokens in it, and a Conv1D with 128 filters with a Kernal size of 5 is passed over it, what’s the output shape?

  • (None, 120, 128)
  • (None, 120, 124)
  • (None, 116, 128)
  • (None, 116, 124)

Question 3: What is the purpose of the embedding dimension?

  • It is the number of words to encode in the embedding
  • It is the number of dimensions required to encode every word in the corpus
  • It is the number of letters in the word, denoting the size of the encoding
  • It is the number of dimensions for the vector representing the word encoding

Question 4: IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?

  • Adam
  • Binary Gradient descent
  • Categorical crossentropy
  • Binary crossentropy

Question 5: If you have a number of sequences of different lengths, how do you ensure that they are understood when fed into a neural network?

  • Use the pad_sequences object from the tensorflow.keras.preprocessing.sequence namespace
  • Make sure that they are all the same length using the pad_sequences method of the tokenizer
  • Process them on the input layer of the Neural Network using the pad_sequences property
  • Specify the input layer of the Neural Network to expect different sizes with dynamic_length

Question 6: When predicting words to generate poetry, the more words predicted the more likely it will end up gibberish. Why?

  • It doesn’t, the likelihood of gibberish doesn’t change
  • Because the probability that each word matches an existing phrase goes down the more words you create
  • Because the probability of prediction compounds, and thus increases overall
  • Because you are more likely to hit words not in the training set

Question 7: What is a major drawback of word-based training for text generation instead of character-based generation?

  • Word based generation is more accurate because there is a larger body of words to draw from
  • Character based generation is more accurate because there are less characters to predict
  • There is no major drawback, it’s always better to do word-based training
  • Because there are far more words in a typical corpus than characters, it is much more memory intensive

Question 8: How does an LSTM help understand meaning when words that qualify each other aren’t necessarily beside each other in a sentence?

  • They load all words into a cell state
  • They don’t
  • They shuffle the words randomly
  • Values from earlier words can be carried to later ones via a cell state

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