[AI] Cousera Quiz
What does a Loss function do ?
- measures how good the current guess is
What does the optimizer do?
What is Convergence?
- The process of getting very close to the correct answer
What does model.fit do?
- It trains the neural network to fit one set of values to another
What is a Convolution?
- A technique to isolate features in images
What is a Pooling?
- A technique to reduce the information in an image while maintaining features
How do Convolutions improve image recognition?
- They isolate features in images
After passing a 3x3 filter over a 28x28 image, how big will the output be?
- 26X26
After max pooling a 26x26 image with a 2x2 filter, how big will the output be?
- 13X13
Applying Convolutions on top of our Deep neural network will make training:
- It depends on many factors. It might make your training faster or slower, and a poorly designed Convolutional layer may even be less efficient than a plain DNN!
Using Image Generator, how do you label images?
- It’s based on the directory the image is contained in
What method on the Image Generator is used to normalize the image?
- rescale
How did we specify the training size for the images?
- the target_size parameter on the training generator
When we specify the input_shape to be (300, 300, 3), what does that mean?
- Every Image will be 300x300 pixels, with 3 bytes to define color
f your training data is close to 1.000 accuracy, but your validation data isn’t, what’s the risk here?
- You’re overfitting on your training data
Convolutional Neural Networks are better for classifying images like horses and humans because:
- All of the above
After reducing the size of the images, the training results were different. Why?
- We removed some convolutions to handle the smaller images
What is the name of the object used to tokenize sentences?
- Tokenizer
What is the name of the method used to tokenize a list of sentences?
- fit_on_texts(sentences)
Once you have the corpus tokenized, what’s the method used to encode a list of sentences to use those tokens?
- texts_to_sequences(sentences)
When initializing the tokenizer, how to you specify a token to use for unknown words?
- oov_token=
If you don’t use a token for out of vocabulary words, what happens at encoding?
- The word isn’t encoded, and is skipped in the sequence
If you have a number of sequences of different lengths, how do you ensure that they are understood when fed into a neural network?
- They’ll get padded to the length of the longest sequence by adding zeros to the beginning of shorter ones
When padding sequences, if you want the padding to be at the end of the sequence, how do you do it?
- Pass padding=’post’ to pad_sequences when initializing it
What is the name of the TensorFlow library containing common data that you can use to train and test neural networks?
- Tensorflow Datasets
How many reviews are there in the IMDB dataset and how are they split?
- 50,000 records, 50/50 train/test split
How are the labels for the IMDB dataset encoded?
- Reviews encoded as a number 0-1
What is the purpose of the embedding dimension?
- It is the number of dimensions for the vector representing the word encoding
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
To use word embeddings in TensorFlow, in a sequential layer, what is the name of the class?
- tf.keras.layers.Embedding
IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?
- Binary crossentropy
When using IMDB Sub Words dataset, our results in classification were poor. Why?
Why does sequence make a large difference when determining semantics of language?
- Because the order in which words appear dictate their impact on the meaning of the sentence
How do Recurrent Neural Networks help you understand the impact of sequence on meaning?
- They carry meaning from one cell to the next
How does an LSTM help understand meaning when words that qualify each other aren’t necessarily beside each other in a sentence?
- Values from earlier words can be carried to later ones via a cell state
What keras layer type allows LSTMs to look forward and backward in a sentence?
- Bidirectional
What’s the output shape of a bidirectional LSTM layer with 64 units?
- (None, 128)
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
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, 116, 128)
What is the name of the method used to tokenize a list of sentences?
- fit_to_text(sentences)
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, 116, 128)
What is the purpose of the embedding dimension?
- It is the number of dimensions for the vector representing the word encoding
IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?
- Binary crossentropy
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
When predicting words to generate poetry, the more words predicted the more likely it will end up gibberish. Why?
- Because the probability that each word matches an existing phrase goes down the more words you create
What is a major drawback of word-based training for text generation instead of character-based generation?
- Because there are far more words in a typical corpus than characters, it is much more memory intensive
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