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on 26-Oct-2025 (Sun)

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Flashcard 7765550566668

Tags
#tensorflow #tensorflow-certificate
Question

# Create X and y values (features and labels)

y = insurance_one_hot[...]

X = insurance_one_hot.drop('charges', axis=1)

Answer
['charges']

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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# Create X and y values (features and labels) y = insurance_one_hot['charges'] X = insurance_one_hot.drop('charges', axis=1)

Original toplevel document

TfC_01_FINAL_EXAMPLE.ipynb
r example: Use pandas get_dummies() function insurance_one_hot = pd.get_dummies(insurance,dtype="int32") #to avoid bool which generate problem with model fitting in TensorFlow insurance_one_hot <span># Create X and y values (features and labels) y = insurance_one_hot['charges'] X = insurance_one_hot.drop('charges', axis=1) #y = y.values # This is not necessary #X = X.values #X, y, X.shape, y.shape # Create training and test datasets #my way: from sklearn.model_selection import train_test_split X_train, X_






Flashcard 7765554236684

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
The RNN has a [...] hidden state, which summarizes task-relevant information from the entire history and is updated at each timestep as well
Answer
multidimensional

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
The RNN has a multidimensional hidden state, which summarizes task-relevant information from the entire history and is updated at each timestep as well

Original toplevel document (pdf)

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