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Annotation 7791910718732

#feature-engineering #lstm #recurrent-neural-networks #rnn
Feature engineering is not only a time- consuming process, it is also error-prone, complex, and highly dependent on the analyst's domain knowledge (or, sometimes, lack thereof)
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used 274 features to predict customer behaviors in a non-contractual setting. One of the authors, who has extensive industry experience, has built predictive models with 600 features and more. <span>Feature engineering is not only a time- consuming process, it is also error-prone, complex, and highly dependent on the analyst's domain knowledge (or, sometimes, lack thereof) <span>

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Annotation 7791912553740

#English #sentence_mining #vocabulary
The critics were abysmally wrong on almost every point. [ in an extreme and bad way:]
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It was an abysmally poor interview. The critics were abysmally wrong on almost every point. [ in an extreme and bad way:]

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abysmally adverb uk /əˈbɪz.məl.i/ us /əˈbɪz.məl.i/ Add to word list in an extreme and bad way: It was an abysmally poor interview. The critics were abysmally wrong on almost every point.



Flashcard 7791914126604

Tags
#English #sentence_mining #vocabulary
Question
The critics were [...] wrong on almost every point. [ in an extreme and bad way:]
Answer
abysmally

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The critics were abysmally wrong on almost every point. [ in an extreme and bad way:]

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abysmally adverb uk /əˈbɪz.məl.i/ us /əˈbɪz.məl.i/ Add to word list in an extreme and bad way: It was an abysmally poor interview. The critics were abysmally wrong on almost every point.






Flashcard 7791915961612

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
In e-commerce, available data sources and prediction scenarios often change, making the generality of RNNs appealing as no [...] feature engineering has to take place.
Answer
problem-specific

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In e-commerce, available data sources and prediction scenarios often change, making the generality of RNNs appealing as no problem-specific feature engineering has to take place.

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Flashcard 7791918583052

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
This particular individual makes a transaction in the first week, followed by one week of inactivity, then transacting for two [...] weeks, and so on; in weeks 3 and 4 they also received some form of a marketing appeal.
Answer
consecutive

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This particular individual makes a transaction in the first week, followed by one week of inactivity, then transacting for two consecutive weeks, and so on; in weeks 3 and 4 they also received some form of a marketing appeal.

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Flashcard 7791921204492

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question

The learning mechanism of the recurrent neural network thus involves:

...

(3) the optimization algorithm, that changes the parameters of the RNN based on the [...].

Answer
gradient

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The learning mechanism of the recurrent neural network thus involves: ... (3) the optimization algorithm, that changes the parameters of the RNN based on the gradient.

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Flashcard 7791923301644

Tags
#tensorflow #tensorflow-certificate
Question
F1-score

Combination of [...] and recall, ususally a good overall metric for classification models.

Answer
precision

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F1-score Combination of precision and recall, ususally a good overall metric for classification models.

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TfC_02_classification-PART_2
anced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. <span>F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metr