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

3.1.3 Practical Considerations When Scaling

There are some practical considerations when scaling sequence data.

Save Coefficients
You will need to scale new data in the future in exactly the same way as the data used to train your model. Save the coefficients used to file and load them later when you need to scale new data when making predictions.
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the training data. Inspect these first-cut estimates and use domain knowledge or domain experts to help improve these estimates so that they will be usefully correct on all data in the future. <span>Save Coefficients You will need to scale new data in the future in exactly the same way as the data used to train your model. Save the coefficients used to file and load them later when you need to scale new data when making predictions. Data Analysis Use data analysis to help you better understand your data. For example, a simple histogram can help you quickly get a feeling for the distribution of quantities to see if

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