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Flashcard 7642468191500

Tags

#causality #statistics

Question

Regular Bayesian networks are purely [...] models, so we can only talk about the flow of association in Bayesian networks.

Answer

statistical

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Regular Bayesian networks are purely statistical models, so we can only talk about the flow of association in Bayesian networks.

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Flashcard 7642474745100

Tags

#causality #statistics

Question

We will denote [...] of 𝑋 by de(𝑋)

Answer

descendants

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We will denote descendants of 𝑋 by de(𝑋)

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Flashcard 7642481036556

Tags

#Data #GAN #reading #synthetic

Question

In generating synthesised data, normally we use the finest [...]. For instance, order_id would represent a store managing orders, or person_id could represent a population.

Answer

granularity

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In generating synthesised data, normally we use the finest granularity. For instance, order_id would represent a store managing orders, or person_id could represent a population.

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The Stan development crew has made it easy to interactively explore diagnostics via the shinystan package, and one should do so with each model. In addition, there are other diagnostics available in other packages like loo and [...].

Answer

posterior

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Stan - diagnostic packages
e it easy to interactively explore diagnostics via the shinystan package, and one should do so with each model. In addition, there are other diagnostics available in other packages like loo and posterior.

Annotation 7668612336908

#feature-engineering #lstm #recurrent-neural-networks #rnn

The LSTM neural network, which we introduce next, is a kind of RNN that has been modified to effectively capture long-term dependencies in the data

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ly. Hence, the effects of marketing actions tend to carry- over into numerous subsequent periods (Lilien, Rangaswamy, & De Bruyn, 2013; Schweidel & Knox, 2013; Van Diepen et al., 2009). The LSTM neural network, which we introduce next, is a kind of RNN that has been modified to effectively capture long-term dependencies in the data

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Flashcard 7668614696204

Question

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 [...] for particular CX investments and directly tie CX initiatives to business outcomes

Answer

ROI

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res. 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 business outcomes

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Annotation 7668616793356

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

The LSTM input layer is defined by the input shape argument on the first hidden layer.

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section lists some final tips to help you when preparing your input data for LSTMs. The LSTM input layer must be 3D. The meaning of the 3 input dimensions are: samples, time steps and features. The LSTM input layer is defined by the input shape argument on the first hidden layer. The input shape argument takes a tuple of two values that define the number of time steps and features. The number of samples is assumed to be 1 or more. The reshape() function on NumPy

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Annotation 7668618890508

#feature-engineering #lstm #recurrent-neural-networks #rnn

The RNN has a multidimensional hidden state, which summarizes task-relevant information from the entire history and is updated at each timestep as well

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the sequence. The RNN processes a sequence of input vectors (x 1 ,x 2 ,x 3 , …,x T ), with each vector being input into the RNN model at its corresponding timestep or position in the sequence. The RNN has a multidimensional hidden state, which summarizes task-relevant information from the entire history and is updated at each timestep as well.

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Flashcard 7668621774092

Tags

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

Question

Batch : A [...] through a subset of samples in the training dataset after which the network weights are updated. One epoch is comprised of one or more batches

Answer

pass

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Batch : A pass through a subset of samples in the training dataset after which the network weights are updated. One epoch is comprised of one or more batches

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Flashcard 7668623871244

Tags

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

Question

the 4 different types of sequence prediction problems: 1. Sequence Prediction. 2. Sequence Classification. 3. Sequence Generation. 4. [...] Prediction

Answer

Sequence-to-Sequence

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the 4 different types of sequence prediction problems: 1. Sequence Prediction. 2. Sequence Classification. 3. Sequence Generation. 4. Sequence-to-Sequence Prediction

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Annotation 7668625706252

[unknown IMAGE 7101511240972]

#has-images #recurrent-neural-networks #rnn

This property makes our model extremely flexible in dealing with diverse customer behaviors observed across multiple contexts and platforms

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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

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Annotation 7668627541260

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

Like RNNs, the LSTMs have recurrent connections so that the state from previous activations of the neuron from the previous time step is used as context for formulating an output. But unlike other RNNs, the LSTM has a unique formulation that allows it to avoid the problems that prevent the training and scaling of other RNNs

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> The LSTM network is different to a classical MLP. Like an MLP, the network is comprised of layers of neurons. Input data is propagated through the network in order to make a prediction. Like RNNs, the LSTMs have recurrent connections so that the state from previous activations of the neuron from the previous time step is used as context for formulating an output. But unlike other RNNs, the LSTM has a unique formulation that allows it to avoid the problems that prevent the training and scaling of other RNNs. This, and the impressive results that can be achieved, are the reason for the popularity of the technique

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Flashcard 7668629376268

Tags

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

Question

[...] gradient descent with a batch size of 32 is a common configuration for LSTMs.

Answer

Mini-batch

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Mini-batch gradient descent with a batch size of 32 is a common configuration for LSTMs.

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Annotation 7668631211276

#recurrent-neural-networks #rnn

Even advanced BTYD models can be too restrictive to adequately capture diverse customer behaviors in different contexts and the derived forecasts present customer future in an oftentimes too simplified way

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bersome and an approach to account for time-varying covariates has only just recently been introduced by Bachmann, Meierer, and Näf (2021) at the cost of manual labeling and slower performance. Even advanced BTYD models can be too restrictive to adequately capture diverse customer behaviors in different contexts and the derived forecasts present customer future in an oftentimes too simplified way

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Edited, memorised or added to reading queue

on 28-Nov-2024 (Thu)

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