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Annotation 7779608562956

#recurrent-neural-networks #rnn
We show how the proposed deep learning model improves on established models both in terms of individual-level accuracy and overall cohort-level bias.
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We show how the proposed deep learning model improves on established models both in terms of individual-level accuracy and overall cohort-level bias. It also helps managers in capturing seasonal trends and other forms of purchase dynamics that are important to detect in a timely manner for the purpose of proactive customer-base manag

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Flashcard 7779611184396

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question

RNN details

We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last event x T and the [...] (the session start) is provided to the final prediction layer

Answer
prediction time

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of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last event x T and the <span>prediction time (the session start) is provided to the final prediction layer <span>

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Flashcard 7779612757260

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question

RNN details

We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last event x T and the prediction time (the [...]) is provided to the final prediction layer

Answer
session start

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s, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the difference between the last event x T and the prediction time (the <span>session start) is provided to the final prediction layer <span>

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Flashcard 7779613805836

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question

RNN details

We use a simple RNN architecture with a single LSTM layer and ten-dimensional cell states. The hidden state at the last time-step is combined with binary non-history features to make the final prediction in a logistic layer. Thus, the final prediction of the RNN is linear in the learned and non-history features. The non-history features describe time, weekday, and behavioral gender and are also provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the [...] between the last event x T and the prediction time (the session start) is provided to the final prediction layer

Answer
difference

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lso provided to the baseline methods. Instead of absolute timestamps, the time differences ∆(x t−1 , x t ) to the previous inputs x t−1 are fed to the RNN at each time- step t. Furthermore, the <span>difference between the last event x T and the prediction time (the session start) is provided to the final prediction layer <span>

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Flashcard 7779614854412

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question

How we can improve model (in the particular stage of the process)?

# 1. Creating model: add more layers, increase numbers of [...] neurons, change activation functions

Answer
hidden

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How we can improve model (in the particular stage of the process)? # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions

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TfC 01 regression
#### How we can improve model # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions # 2. Compiling: change optimizer or its parameters (eg. learning rate) # 3. Fitting: more epochs, more data ### How? # from smaller model to larger model Evaluating models Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What






Flashcard 7779616427276

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
To forecast future customer behavior, our model is trained using individual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over [...] consecutive discrete time periods
Answer
seven

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ained using individual sequences of past transaction events, i.e., chronological accounts of a customer’s lifetime. The example in Table 2 describes one such customer’s transaction history over <span>seven consecutive discrete time periods <span>

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Annotation 7779619835148

#abm #agent-based #machine-learning #model #priority #synergistic-integration
As demonstrated by Torrens et al. [9], the individual behavior in an agent-based model can be machine-learned from samples collected at the individual-agent level.
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As demonstrated by Torrens et al. [9], the individual behavior in an agent-based model can be machine-learned from samples collected at the individual-agent level. In addition, modern ABM techniques can help in analysis through their ability to have adaptive agents in different changing environments [10]. The machine- learning-based inference mode

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Flashcard 7779621670156

Tags
#deep-learning #keras #lstm #python #sequence
Question
Normalization requires that you know or are able to accurately estimate the [...] observable values.
Answer
minimum and maximum

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Normalization requires that you know or are able to accurately estimate the minimum and maximum observable values.

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