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Flashcard 7628366679308

Question
The algorithms generate predictive scores for each customer based on journey features. These scores allow the company to predict individual customer [...] and value outcomes such as revenue, loyalty, and cost to serve. More broadly, they allow CX leaders to assess the ROI for particular CX investments and directly tie CX initiatives to business outcomes
Answer
satisfaction

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The algorithms generate predictive scores for each customer based on journey features. These scores allow the company to predict individual customer satisfaction and value outcomes such as revenue, loyalty, and cost to serve. More broadly, they allow CX leaders to assess the ROI for particular CX investments and directly tie CX initiatives to bu

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Flashcard 7628378213644

Tags
#DAG #causal #edx #has-images
[unknown IMAGE 7093205732620]
Question
In those cases, it is generally better [...] L, because even though adjusting for L will not eliminate all confounding by U, it will typically eliminate some of the confounding by U
Answer
to adjust for

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In those cases, it is generally better to adjust for L, because even though adjusting for L will not eliminate all confounding by U, it will typically eliminate some of the confounding by U

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Flashcard 7628562500876

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #retail #simulation #synthetic-data
Question
In this paper, we present our study of consumer purchase behaviour, wherein, we establish a data-driven framework to predict whether a consumer is going to purchase an item within a certain time frame using e-commerce retail data. To model this relationship, we create a sequential time-series data for all relevant [...] combinations. We then build generalized non-linear models by generating features at the intersection of consumer, item, and time.
Answer
consumer-item

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ct whether a consumer is going to purchase an item within a certain time frame using e-commerce retail data. To model this relationship, we create a sequential time-series data for all relevant <span>consumer-item combinations. We then build generalized non-linear models by generating features at the intersection of consumer, item, and time. <span>

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Flashcard 7673746689292

Tags
#Shell #linux
Question

How to Force User to Change Password at Next Login in Linux

User name: ravi


# passwd --expire [...]
Answer
ravi

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How to Force User to Change Password at Next Login in Linux User name: ravi # passwd --expire ravi

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How to Force User to Change Password at Next Login in Linux
User name: ravi # passwd --expire ravi How to Force User to Change Password at Next Login in Linux Using passwd Command To force a user to change his/her password, first of all the password must have expired and to cause a u






Annotation 7673748262156

#deep-learning #keras #lstm #python #sequence
constant error carrousel or CEC (acronym)
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te and input gate are used in the updating of the internal state. The output gate is a final limiter on what the cell actually outputs. It is these gates and the consistent data flow called the <span>constant error carrousel or CEC that keep each cell stable (neither exploding or vanishing). Each memory cell’s internal architecture guarantees constant error flow within its constant error carrousel CEC... This repr

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Annotation 7673750621452

#deep-learning #keras #lstm #python #sequence
The key to the memory cell are the gates. These too are weighted functions that further govern the information flow in the cell.
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1.4.2 LSTM Gates The key to the memory cell are the gates. These too are weighted functions that further govern the information flow in the cell. There are three gates: Forget Gate: Decides what information to discard from the cell. Input Gate: Decides which values from the input to update the memory state. Output Gate: Decides w

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Annotation 7673753242892

#deep-learning #keras #lstm #python #sequence
Each epoch can be partitioned into groups of input-output pattern pairs called batches
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The backpropagation algorithm requires that the network be trained for a specified number of epochs or exposures to all sequences in the training dataset. Each epoch can be partitioned into groups of input-output pattern pairs called batches. This defines the number of patterns that the network is exposed to before the weights are updated within an epoch. It is also an efficiency optimization, ensuring that not too many inp

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Flashcard 7673755077900

Tags
#DAG #causal #edx
Question
So all these methods for confounding adjustment -- [...], matching, inverse probability weighting, G-formula, G-estimation -- have two things in common. First, they require data on the confounders that block the backdoor path. If those data are available, then the choice of one of these methods over the others is often a matter of personal taste. Unless the treatment is time-varying -- then we have to go to G-methods
Answer
stratification

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So all these methods for confounding adjustment -- stratification, matching, inverse probability weighting, G-formula, G-estimation -- have two things in common. First, they require data on the confounders that block the backdoor path. If those data a

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Flashcard 7673757175052

Tags
#deep-learning #keras #lstm #python #sequence
Question
How to Convert [...] Data to Numerical Data This involves two steps: 1. Integer Encoding. 2. One Hot Encoding.
Answer
Categorical

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How to Convert Categorical Data to Numerical Data This involves two steps: 1. Integer Encoding. 2. One Hot Encoding.

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