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When to use missing data imputation in the data analysis problem?
5 votes

Generally speaking you have two options: impute the missing data discard the missing data Due to the fact that ML models perform better with more data, the former is usually preferred. However, you ...

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Data augmentation when using flow_from_directory in CNN
Accepted answer
6 votes

Augmentations often rely on the nature of your data. Imagine if an the result of an augmentation would be logical in your context. For example let's say you have a cats vs dogs dataset. The images ...

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Pre-trained CNN for one-shot learning
2 votes

Because the layers in the CNNs must be already able to extract features from images. Normally this procedure takes thousands of iterations to be completed, so if they were not trained we could't ...

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