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Data is not flat


Feature engineering and model training often comes hand in hand. Some tasks have an overwhelming amount of high dimensional data, some tasks have little data or very low-dimension data. This talk targets the latter problem: what can be done with the data itself to significantly improve the model performance and when manual feature engineering does make sense.
A sample case of Classification problem with NN will be presented The goal of the talk is to remind about something every person working with the data thinks and probably uses. Slides, Jupyter notebook with the example, test and train sets, NN configuration file are available on: lisa/conferences


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