on 01-Feb-2023 (Wed)

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Annotation 7545563254028

 #ML-engineering #ML_in_Action #learning #machine #software-engineering Data scientists are also expected to be familiar with additional realms of competency. From mid-level DE skills (you have to get your data for your data science from somewhere, right?), software development skills, project management skills, visualization skills, and presentation skills, the list grows ever longer, and the volumes of experience that need to be gained become rather daunting. It’s not much of a surprise, considering all of this, that “just figuring it out” in reference to all the required skills to create production-grade ML solutions is untenable
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Annotation 7560775994636

 #recurrent-neural-networks #rnn it also accurately predicts periods of elevated transaction activity and captures other forms of purchase dynamics that can be leveraged in simulations of future sequences of customer transactions.
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As we have shown, it also accurately predicts periods of elevated transaction activity and captures other forms of purchase dynamics that can be leveraged in simulations of future sequences of customer transactions. We highlight our model’s flexibility and performance on two groups of valuable customers: those who keep making more and more transactions with the firm (denoted as ”opportunity” custom

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Flashcard 7560777829644

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
The two calendar components – the month and week indicators – represent [...] contextual information which is shared across the individuals within a given cohort. In addition, in this example, we include also an individual time-invariant covariate (gender) and a time-varying, individual-level covariate (marketing appeals)
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time-varying

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The two calendar components – the month and week indicators – represent time-varying contextual information which is shared across the individuals within a given cohort. In addition, in this example, we include also an individual time-invariant covariate (gender) and a

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Annotation 7560780188940

 [unknown IMAGE 7101511240972] #has-images #recurrent-neural-networks #rnn Note that the model is completely agnostic about further extensions: all individual-level, cohort-level, time-varying, or time-invariant covariates are simply encoded as categorical input variables, and are handled equally by the model
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al-level covariates are strictly optional – in our empirical study, the Base model is built without any such variables. Whenever individual covariates are included, we label the model Extended. <span>Note that the model is completely agnostic about further extensions: all individual-level, cohort-level, time-varying, or time-invariant covariates are simply encoded as categorical input variables, and are handled equally by the model. This property makes our model extremely flexible in dealing with diverse customer behaviors observed across multiple contexts and platforms <span>

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Flashcard 7560782548236

Tags
#has-images
Question
library(ggcharts)
(p <- bar_chart(cyl, cyl, pct))

Next, let’s try to change the axis labels to include a percentage sign using the ...

p + scale_y_continuous(labels = [...]::percent)

Answer
scales

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library(ggcharts) (p <- bar_chart(cyl, cyl, pct)) Copy Next, let’s try to change the axis labels to include a percentage sign using the ... p + scale_y_continuous(labels = scales::percent) Copy

Flashcard 7560785431820

Tags
#deep-learning #keras #lstm #python #sequence
Question
If the number of input and output time steps vary, then an [...] architecture can be used. The input time steps are mapped to a fixed sized internal representation of the sequence, then this vector is used as input to producing each time step in the output sequence
Answer
Encoder-Decoder

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If the number of input and output time steps vary, then an Encoder-Decoder architecture can be used. The input time steps are mapped to a fixed sized internal representation of the sequence, then this vector is used as input to producing each time step in the

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Flashcard 7560788053260

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
In machine learning, a [...] refers to a variable that describes some aspect of individual data objects (Dong & Liu, 2018).
Answer
feature

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In machine learning, a feature refers to a variable that describes some aspect of individual data objects (Dong & Liu, 2018).

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