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Flashcard 7656710212876

Tags
#tensorflow #tensorflow-certificate
Question
Getting dataset ready for tensorflow
  1. Converting non-numerical columns

For example: Use pandas get_dummies() function

insurance_one_hot = pd.get_dummies(insurance,dtype="int32") #to avoid [...] which generate problem with model fitting in TensorFlow
insurance_one_hot
Answer
bool

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Getting dataset ready for tensorflow Converting non-numerical columns For 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>

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TfC_01_FINAL_EXAMPLE.ipynb
Getting dataset ready for tensorflow Converting non-numerical columns For 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 # 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






Flashcard 7656885849356

Tags
#algebra #matrix #tensorflow #tensorflow-certificate
Question

In General:

To [...] an m×n matrix by an n×p matrix, the ns must be the same,
and the result is an m×p matrix.

Answer
multiply

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Matrix multiplication
In General: To multiply an m×n matrix by an n×p matrix, the ns must be the same, and the result is an m×p matrix.






Annotation 7662010764556

#recurrent-neural-networks #rnn
With each vector read as input, the model’s training objective is to predict the target variable, which in this self- supervised training setup is just the input variable shifted by a single time step.
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the discrete time periods, then a softmax layer with four neurons is sufficient: the neurons’ respective outputs represent the inferred probability of zero, one, two and three transactions. 10 <span>With each vector read as input, the model’s training objective is to predict the target variable, which in this self- supervised training setup is just the input variable shifted by a single time step. Using the example from Table 2, given the sequence of input vectors starting with the first week of January, i.e. [1,January,1,F,0], [0,January,2,F,0], [1,Jan- uary,3,F,1] ..., we train

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Flashcard 7662012599564

Tags
#causality #has-images #statistics


Question

We assumed 𝑋 is discrete when we summed over its values, but we can simply replace the sum with an integral if 𝑋 is continuous. Throughout this book, that will be the case, so we usually won’t point it out

To jest [...] równanie.

Answer
kluczowe

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rete when we summed over its values, but we can simply replace the sum with an integral if 𝑋 is continuous. Throughout this book, that will be the case, so we usually won’t point it out To jest <span>kluczowe równanie. <span>

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Annotation 7662014958860

#feature-engineering #lstm #recurrent-neural-networks #rnn
Essentially, it (feature engeenering) refers to the process of using domain knowledge to create useful features that can be fed as predictors into a model.
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cribes some aspect of individual data objects (Dong & Liu, 2018). Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation. <span>Essentially, it refers to the process of using domain knowledge to create useful features that can be fed as predictors into a model. <span>

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Flashcard 7662017580300

Tags
#deep-learning #keras #lstm #python #sequence
Question
By default, the samples within an epoch are shuffled. This is a good practice when working with Multilayer Perceptron neural networks. If you are trying to preserve state across samples, then the [...] of samples in the training dataset may be important and must be preserved. This can be done by setting the shuffle argument in the fit() function to False.
Answer
order

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default, the samples within an epoch are shuffled. This is a good practice when working with Multilayer Perceptron neural networks. If you are trying to preserve state across samples, then the <span>order of samples in the training dataset may be important and must be preserved. This can be done by setting the shuffle argument in the fit() function to False. <span>

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