Edited, memorised or added to reading queue

on 01-Feb-2023 (Wed)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

#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
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




#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.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
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

Original toplevel document (pdf)

cannot see any pdfs




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)
Answer
time-varying

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 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

Original toplevel document (pdf)

cannot see any pdfs







[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
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
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>

Original toplevel document (pdf)

cannot see any pdfs




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

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

Open it
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

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
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

Original toplevel document (pdf)

cannot see any pdfs







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

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

Original toplevel document (pdf)

cannot see any pdfs