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on 26-Jun-2024 (Wed)

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Flashcard 7642345508108

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#feature-engineering #has-images #lstm #recurrent-neural-networks #rnn
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Question
Fig. 1. Four customers with markedly different purchase [...] but identical features in terms of recency (last purchase), frequency (number of purchases), and seniority (first purchase
Answer
patterns

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Fig. 1. Four customers with markedly different purchase patterns but identical features in terms of recency (last purchase), frequency (number of purchases), and seniority (first purchase

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Annotation 7642348129548

#deep-learning #keras #lstm #python #sequence
[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
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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 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 o

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Annotation 7642350226700

#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.
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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 inc

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