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That's because making a machine-learning system more accurate requires feeding it lots of example data to tune its abilities.
Most AI approaches involve simply feeding a computer huge quantities of example data and having it generate its own examples after that.
In deep learning, vast quantities of example data are used to train a large simulated neural network to perform a tricky task like recognizing a particular person in a photo or deciphering the words in speech.
We apply DayFilter on 474 days of example data from an international school campus in a tropical climate and 407 days of data from an office building from a temperate European climate.
That takes two things: a ton of example data collected and organized by artificial intelligence and humans to accurately label and verify that data.
Additional examples showing the conversion of example data from PubChem, the Dortmund Data Bank, and a CIF file are also included on this page along with XML Stylesheet Language Transformation (XSLT) [58] files used to convert them.
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To increase the reality of the example, data are gathered through experts' opinions.
In particular, we show the value of organizing the example data according to object category templates.
Participants were also asked to think of other examples of data use.
The richness of the examples of data use is very informative.
All of these examples of data ambiguity (discordant or insufficient published data) highlight gaps in knowledge for further study.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com