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Annotation 7103944985868

#feature-engineering #lstm #recurrent-neural-networks #rnn
Third, while LSTM models offer a markedly improved solution to the problem of exploding gradients (over vanilla RNN models), they are not guaranteed to be shielded from it entirely. Facing such an issue, the analyst might need to rely on computational tricks, such as gradient clipping (Bengio, 2012), gradient scaling, or batch normalization (Bjorck, Gomes, Selman, & Weinberger, 2018)
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Annotation 7103994531084

3.1.2 Standardize Series Data
#deep-learning #keras #lstm #python #sequence

3.1.2 Standardize Series Data

Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1. This can be thought of as subtracting the mean value or centering the data. Like normalization, standardization can be useful, and even required in some machine learning algorithms when your data has input values with differing scales. Standardization assumes that your observations fit a Gaussian distribution (bell curve) with a well behaved mean and standard deviation. You can still standardize your time series data if this expectation is not met, but you may not get reliable results.
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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 7628564073740

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
feature engineering: a [...] of identifiers f i has to be designed to capture the essence of an individual consumer history. Only signals that are encoded in the feature vector can be picked up by the prediction model. Defining expressive features often requires both, domain knowledge as well as data-science intuition
Answer
fixed set

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feature engineering: a fixed set of identifiers f i has to be designed to capture the essence of an individual consumer history. Only signals that are encoded in the feature vector can be picked up by the prediction mo

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Annotation 7628566957324

#deep-learning #keras #lstm #python #sequence
Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1
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3.1.2 Standardize Series Data Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1. This can be thought of as subtracting the mean value or centering the data. Like normalization, standardization can be useful, and even required in some machine learning algorithms whe

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Flashcard 7628569840908

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
Third, while [...] models offer a markedly improved solution to the problem of exploding gradients (over vanilla RNN models), they are not guaranteed to be shielded from it entirely. Facing such an issue, the analyst might need to rely on computational tricks, such as gradient clipping (Bengio, 2012), gradient scaling, or batch normalization (Bjorck, Gomes, Selman, & Weinberger, 2018)
Answer
LSTM

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Third, while LSTM models offer a markedly improved solution to the problem of exploding gradients (over vanilla RNN models), they are not guaranteed to be shielded from it entirely. Facing such an issue,

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Flashcard 7628571675916

Question

How do you check the login tokens for all running jupyter notebook instances?

jupyter [...] list

Answer
notebook

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How do you check the login tokens for all running jupyter notebook instances? jupyter notebook list






Flashcard 7628573510924

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
When an analyst uses feature engineering to predict behavior, the performance of the model will depend greatly on the analyst's [...] knowledge
Answer
domain

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When an analyst uses feature engineering to predict behavior, the performance of the model will depend greatly on the analyst's domain knowledge

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