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Annotation 7106689633548

#Data #GAN #reading #synthetic

We end up with the following attributes for the parent and child components:

| | | | | --- | --- | --- |Parent attributes | Name | Type | Description | | Employee_id | Categorical | 78 levels | | order_date | Datetime | 1996-1998 | | required_date | Datetime | 1996-1998 | | shipped_date | Datetime | 1996-1998 | | ship_via | Categorical | 3 levels | | freight | Numerical | [0.02 - 1007] | | ship_region | Categorical | 19 levels | | ship_country | Categorical | 21 levels | | customer_city | Categorical | 70 levels | | customer_region | Categorical | 19 levels | | customer_country | Categorical | 21 levels | | order_lenght | Numerical | [1 - 22] | | | | | | --- | --- | --- |Table 3: Child attributes Name Type Description product_id Categorical 77 levels supplier_id Categorical 29 levels category_id Categorical 8 levels unit_price Numerical [2 - 263.5] quantity_per_unit Numerical [0 - 70]

All data is converted into a one-hot encoding, including datetime and numerical types (previously binned into 20 levels).

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hierarchical GAN Data processingΒΆ The first step in data processing is to remove all personally identifiable information β€” like names and addresses β€” and any free-text fields. We end up with the following attributes for the parent and child components: | | | | | --- | --- | --- |Parent attributes | Name | Type | Description | | Employee_id | Categorical | 78 levels | | order_date | Datetime | 1996-1998 | | required_date | Datetime | 1996-1998 | | shipped_date | Datetime | 1996-1998 | | ship_via | Categorical | 3 levels | | freight | Numerical | [0.02 - 1007] | | ship_region | Categorical | 19 levels | | ship_country | Categorical | 21 levels | | customer_city | Categorical | 70 levels | | customer_region | Categorical | 19 levels | | customer_country | Categorical | 21 levels | | order_lenght | Numerical | [1 - 22] | | | | | | --- | --- | --- |Table 3: Child attributes Name Type Description product_id Categorical 77 levels supplier_id Categorical 29 levels category_id Categorical 8 levels unit_price Numerical [2 - 263.5] quantity_per_unit Numerical [0 - 70] All data is converted into a one-hot encoding, including datetime and numerical types (previously binned into 20 levels). One-hot encoding converts categorical variables (occupation, city names, etc) into a numeric format to be ingested by machine learning algorithms. The final generated tabular data is converted back into tables to recreate the original database

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Annotation 7583426546956

#feature-engineering #lstm #recurrent-neural-networks #rnn
We apply the LSTM neural networks to predict customer responses in direct marketing and discuss its possible application in other contexts within marketing, such as market-share forecasting using scanner data, churn prediction, or predictions using clickstream data
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ely do away with the complicated and time-consuming step of feature engineering, even when applied to highly structured problems such as predicting the future behaviors of a panel of customers. <span>We apply the LSTM neural networks to predict customer responses in direct marketing and discuss its possible application in other contexts within marketing, such as market-share forecasting using scanner data, churn prediction, or predictions using clickstream data <span>

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Annotation 7583428119820

#Inference #causal #reading
the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow leveraging information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016)
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Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow leveraging information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016)

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Flashcard 7583430741260

Tags
#Data #GAN #reading #synthetic
Question

We end up with the following attributes for the parent and child components:

| | | | | --- | --- | --- |Parent attributes | Name | Type | Description | | Employee_id | Categorical | 78 levels | | order_date | Datetime | 1996-1998 | | required_date | Datetime | 1996-1998 | | shipped_date | Datetime | 1996-1998 | | ship_via | Categorical | 3 levels | | freight | Numerical | [0.02 - 1007] | | ship_region | Categorical | 19 levels | | ship_country | Categorical | 21 levels | | customer_city | Categorical | 70 levels | | customer_region | Categorical | 19 levels | | customer_country | Categorical | 21 levels | | order_lenght | Numerical | [1 - 22] | | | | | | --- | --- | --- |Table 3: Child attributes Name Type Description product_id Categorical 77 levels supplier_id Categorical 29 levels category_id Categorical 8 levels unit_price Numerical [2 - 263.5] quantity_per_unit Numerical [0 - 70]

All data is converted into a one-hot encoding, including [...] and numerical types (previously binned into 20 levels).

Answer
datetime

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supplier_id Categorical 29 levels category_id Categorical 8 levels unit_price Numerical [2 - 263.5] quantity_per_unit Numerical [0 - 70] All data is converted into a one-hot encoding, including <span>datetime and numerical types (previously binned into 20 levels). <span>

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Flashcard 7583432838412

Tags
#causality #has-images #statistics


Question
if the DAG were simply two connected nodes 𝑋 and π‘Œ as in Figure 3.8, the local Markov assumption would tell us that we can factorize 𝑃(π‘₯, 𝑦) as 𝑃(π‘₯)𝑃(𝑦|π‘₯) , but it would also allow us to factorize 𝑃(π‘₯, 𝑦) as 𝑃(π‘₯)𝑃(𝑦) , meaning it allows distributions where 𝑋 and π‘Œ are independent. In contrast, the minimality assumption does [...] this additional independence
Answer
not allow

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𝑃(π‘₯, 𝑦) as 𝑃(π‘₯)𝑃(𝑦|π‘₯) , but it would also allow us to factorize 𝑃(π‘₯, 𝑦) as 𝑃(π‘₯)𝑃(𝑦) , meaning it allows distributions where 𝑋 and π‘Œ are independent. In contrast, the minimality assumption does <span>not allow this additional independence <span>

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Flashcard 7583434673420

Tags
#deep-learning #keras #lstm #python #sequence
Question
The [...] LSTM is a model that has multiple hidden LSTM layers where each layer contains multiple memory cells.
Answer
Stacked

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The Stacked LSTM is a model that has multiple hidden LSTM layers where each layer contains multiple memory cells.

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Flashcard 7583436508428

Tags
#deep-learning #keras #lstm #python #sequence
Question
The internal state in LSTM layers is also accumulated when evaluating a network and when making [...]
Answer
predictions

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The internal state in LSTM layers is also accumulated when evaluating a network and when making predictions

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