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Annotation 7103921130764

#feature-engineering #lstm #recurrent-neural-networks #rnn
At each timestep, we submit relevant variables x, such as marketing actions (e.g., solicitations), customer behaviour (e.g., purchase occurrences), and seasonality indicators (e.g., month), in the form of a vector of dummy variables. In our illustration, the y variable is a vector of size one that indicates whether the customer has purchased during the following period. However, the dependent variable can easily include multiple indicators.
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Flashcard 7590036245772

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
At each timestep, we submit relevant variables x, such as marketing actions (e.g., solicitations), customer behaviour (e.g., purchase occurrences), and seasonality indicators (e.g., month), in the form of a vector of dummy variables. In our illustration, the y variable is a vector of size one that indicates whether the customer has purchased during the following period. However, the dependent variable can easily include [...].
Answer
multiple indicators

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es. In our illustration, the y variable is a vector of size one that indicates whether the customer has purchased during the following period. However, the dependent variable can easily include <span>multiple indicators. <span>

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Annotation 7590038342924

#ggplot2

Something is not right here! 4000%!? That seems a bit excessive. The problem here is that by default scales::percent() multiplies its input value by 100. This can be controlled by the scale parameter.

scales::percent(100, scale = 1)
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Something is not right here! 4000%!? That seems a bit excessive. The problem here is that by default scales::percent() multiplies its input value by 100. This can be controlled by the scale parameter. scales::percent(100, scale = 1) Copy ## [1] "100%" However, scale_y_continuous() expects a function as input for its labels parameter not the actual labels itself. Thus, using percent() is not an option anymore. Fortu



Annotation 7590040177932

#feature-engineering #lstm #recurrent-neural-networks #rnn
Artificial feedforward neural networks (ANN) have also been shown to effectively predict household brand choices, as well as brand market shares (Agrawal & Schorling, 1996). Since brand choices can be modeled as sequential choices, and data complexity increases exponentially with the number of brands (with interaction effects), LSTM neural networks offer suitable alternatives.
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rs and brand managers alike. The multinomial logit model (MNL) is commonly used to predict brand choice and market share using marketing-mix and loyalty variables (Guadagni & Little, 1983). <span>Artificial feedforward neural networks (ANN) have also been shown to effectively predict household brand choices, as well as brand market shares (Agrawal & Schorling, 1996). Since brand choices can be modeled as sequential choices, and data complexity increases exponentially with the number of brands (with interaction effects), LSTM neural networks offer suitable alternatives. Similar to our studies, we could encode brand choices and the decision environment as we encoded solicitations and donations: as a multidimensional vector. We conjecture that testing th

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Flashcard 7590042537228

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Consumer behaviour in e-commerce can be described by sequences of interactions with a webshop. We show that [...] are a natural fit for modelling and predicting consumer behaviour.
Answer
recurrent neural networks (RNNs)

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Consumer behaviour in e-commerce can be described by sequences of interactions with a webshop. We show that recurrent neural networks (RNNs) are a natural fit for modelling and predicting consumer behaviour.

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Flashcard 7590044372236

Tags
#deep-learning #keras #lstm #python #sequence
Question
[the success of deep neural networks] is commonly attributed to the [...] that is introduced due to the several layers. Each layer processes some part of the task we wish to solve, and passes it on to the next. In this sense, the DNN can be seen as a processing pipeline, in which each layer solves a part of the task before passing it on to the next, until finally the last layer provides the output
Answer
hierarchy

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[the success of deep neural networks] is commonly attributed to the hierarchy that is introduced due to the several layers. Each layer processes some part of the task we wish to solve, and passes it on to the next. In this sense, the DNN can be seen as a processi

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Flashcard 7590046207244

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
In this paper, we show that recurrent neural networks (RNNs) are promising to overcome both shortcomings of vector-based methods, tedious [...] and lack of explainability.
Answer
feature engineering

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In this paper, we show that recurrent neural networks (RNNs) are promising to overcome both shortcomings of vector-based methods, tedious feature engineering and lack of explainability.

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Flashcard 7590048042252

Tags
#bayes #programming #r #statistics
Question
The role of Bayesian inference is to compute the exact relative [...] of candidate parameter values, while also taking into account their prior probabilities.
Answer
credibilities

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The role of Bayesian inference is to compute the exact relative credibilities of candidate parameter values, while also taking into account their prior probabilities.

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Annotation 7590066392332

#Language #go #programming
Ending a file's name with _test.go tells the go test command that this file contains test functions.
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Flashcard 7590070062348

Tags
#Language #go #programming
Question
Ending a file's name with _test.go tells the [...] command that this file contains test functions.
Answer
go test

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Ending a file's name with _test.go tells the go test command that this file contains test functions.






Flashcard 7590071110924

Tags
#Language #go #programming
Question
Ending a file's name with _[...] tells the go test command that this file contains test functions.
Answer
test.go

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Ending a file's name with _test.go tells the go test command that this file contains test functions.