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Annotation 7625095122188

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

Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation

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In machine learning, a feature refers to a variable that describes some aspect of individual data objects (Dong & Liu, 2018). Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation. Essentially, it refers to the process of using domain knowledge to create useful features that can be fed as predictors into a model.

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Flashcard 7642445647116

Tags

#causality #statistics

Question

A graph is a collection of nodes (also called “vertices”) and [...] that connect the nodes.

Answer

edges

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A graph is a collection of nodes (also called “vertices”) and edges that connect the nodes.

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Annotation 7642447744268

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

Similar to our studies, we could encode brand choices and the decision environment as we encoded solicitations and donations: as a multidimensional vector.

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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 the performance of LSTM neural networks in the context of brand choices would constitute an exciting replication area.

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Flashcard 7642449579276

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 [...] of vector-based methods, tedious feature engineering and lack of explainability.

Answer

shortcomings

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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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Annotation 7642452725004

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

The RNNs are made of high dimensional hidden states with non-linear dynamics.

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recurrent connections is called recurrent neural networks (RNNs), which are capable of modelling sequential data for sequence recognition, sequence production, and time series prediction [15]. The RNNs are made of high dimensional hidden states with non-linear dynamics.

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Flashcard 7642454560012

Tags

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

Question

Consumer histories are inherently sequential and of varying [...] T , making RNNs a natural model choice.

Answer

lengths

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Consumer histories are inherently sequential and of varying lengths T , making RNNs a natural model choice.

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Annotation 7642456395020

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

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

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3.1. Prepare Numeric Data 3.1.1 Normalize Series Data Normalization is a rescaling of the data from the original range so that all values are within the range of 0 and 1. Normalization requires that you know or are able to accurately estimate the minimum and maximum observable values. You may be able to estimate these values from your available data. If your series is trending up or down, estimating these expected values may be difficult and normalization may not be

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Flashcard 7642459016460

Tags

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

Question

Consumer behavior is inherently sequential which makes [...]s a perfect fit.

Answer

RNN

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Consumer behavior is inherently sequential which makes RNNs a perfect fit.

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Flashcard 7642460327180

Tags

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

Question

For this exercise, the authors developed two separate LSTM models. The first one predicted the likelihood that each donor was going to respond favorably to the solicitation (0/1), and we calibrated it on the entire calibration data (N = 61,928). The second LSTM model predicted the [...] t in case of donation, and we calibrated it on the individuals who donated in the calibration data (N = 6,456)

Answer

donation amount

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ed the likelihood that each donor was going to respond favorably to the solicitation (0/1), and we calibrated it on the entire calibration data (N = 61,928). The second LSTM model predicted the donation amount t in case of donation, and we calibrated it on the individuals who donated in the calibration data (N = 6,456)

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Annotation 7642462162188

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

as part of framing your problem you must split long sequences into subsequences that are both long enough to capture relevant context for making predictions, but short enough to efficiently train the network

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se. This is a sensible approach that can work well in practice, but it is blind to temporal dependencies that span more than 20 time steps. — Training Recurrent Neural Networks, 2013 This means as part of framing your problem you must split long sequences into subsequences that are both long enough to capture relevant context for making predictions, but short enough to efficiently train the network

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Flashcard 7642464521484

Tags

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

Question

LSTM [...] are comprised of weights and gates

Answer

cells

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LSTM cells are comprised of weights and gates

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Flashcard 7642466618636

Tags

#causality #statistics

Question

In [...] data, it is unrealistic to assume that the treatment groups are exchangeable. In other words, there is no reason to expect that the groups are the same in all relevant variables other than the treatment.

Answer

observational

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In observational data, it is unrealistic to assume that the treatment groups are exchangeable. In other words, there is no reason to expect that the groups are the same in all relevant variables other t

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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 7642473172236

Tags

#abm #agent-based #machine-learning #model #priority #synergistic-integration

Question

Traditionally, two modeling methodologies, known as top-down and [...], modeling approaches have been widely applied to model complex systems

Answer

bottom-up

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Traditionally, two modeling methodologies, known as top-down and bottom-up, modeling approaches have been widely applied to model complex systems

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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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Annotation 7642476580108

#Data #GAN #reading #synthetic

In order for synthetic data — completely artificial and private production data — to be useful, it must maintain these relationships (referential integrity) among the data, including preserving the data links that can stretch across multiple tables.Referential integrity

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Referential integrity is a structural element that ensures that the relationships among different sets of data are accurate. In order for synthetic data — completely artificial and private production data — to be useful, it must maintain these relationships among the data, including preserving the data links that can stretch across multiple tables.

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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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Flashcard 7642482347276

Tags

#recurrent-neural-networks #rnn

Question

We propose and implement a flexible methodological framework that provides marketing managers with highly accurate forecasts of fine granularity both in the short and in the long run. Our method also captures [...] and customer-level dynamics and allows to differentiate between different customer groups

Answer

seasonal peaks

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implement a flexible methodological framework that provides marketing managers with highly accurate forecasts of fine granularity both in the short and in the long run. Our method also captures seasonal peaks and customer-level dynamics and allows to differentiate between different customer groups

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Flashcard 7642484968716

Tags

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

Question

Feature engineering has been used broadly to refer to multiple aspects of feature [...], extraction, and transformation

Answer

creation

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Feature engineering has been used broadly to refer to multiple aspects of feature creation, extraction, and transformation

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Annotation 7642486279436

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

In principle, one could evaluate the logistic regression model at every single time-step in the consumer history to determine the influence of individual events

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In principle, one could evaluate the logistic regression model at every single time-step in the consumer history to determine the influence of individual events. However, this would involve the inefficient process of re-calculating features for every time-step. Calculations at timesteps t and t − 1 would be highly redundant: features at t repre

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Flashcard 7642488114444

Question

To create an environment with a specific version of Python and multiple packages including a package with a specific version:

$ conda create -n <env_name> [...]=<version#> <packagename> <packagename> <packagename>=<version#>

Answer

python

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To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#>

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How to Manage Python Dependencies with Conda - ActiveState
rmine the Current Environment with Conda The current or active environment is shown in parentheses () or brackets [] at the beginning of the Anaconda Prompt or terminal: (<current_env>) $ Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#> Alternatively, you can use conda to install all the packages in a requirements.txt file. You can save a requirements.txt file from an existing environment, or manually create a new requirements.txt for a different environment. To create a conda requirements.txt file from an existing environment: Activate your project environment. See section above entitled “How to Activate an Environment with Conda” for detai

Flashcard 7642489425164

Tags

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

Question

We can summarize the 3 key benefits of LSTMs as:

2. Possesses memory to overcome the issues of [...] temporal dependency with input sequences

Answer

long-term

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We can summarize the 3 key benefits of LSTMs as: 2. Possesses memory to overcome the issues of long-term temporal dependency with input sequences

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on 27-Jun-2024 (Thu)

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