Examples of using These data sets in English and their translations into Chinese
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Political
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Ecclesiastic
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Programming
These data sets are usually small.
Look at the pictures these data sets print.
Testing of these data sets would require various tools, techniques and frameworks.
What are the legal and ethical aspects regarding these data sets?
These data sets are often difficult to extract, because they are so deeply nested.
In two years, we anticipate these data sets will grow in size to about 1 petabyte.
These data sets are organized by statistical area, but this is just a starting point.
Imagine if there are tools and mechanisms to make these data sets sharable(through a data commons).
Unfortunately, these data sets are not easy to access or share among scientists.
The models used to analyse data can attempt to take into account the often strong temporal andspatial biases in these data sets.
At the same time, these data sets will be collated and stored for more long-term analysis.
The data exposed in each of these sets would not exist without Facebook, yet these data sets are no longer under Facebook's control.
These data sets are so large and so complex that we can't analyze them using traditional applications.
When machine learning models are applied to these data sets, IT operations transform from being reactive to predictive.
These data sets currently include data from the Human Genome Project, the U.S. Census, Wikipedia.
So if PII is stripped from these data sets(as depicted in the figure below), what's the big deal?
These data sets contain only earthquake magnitudes, locations and times, and leave out the rest of the information.
Without the data dictionary, these data sets could be difficult or even impossible to analyze properly.
These data sets contain only earthquake magnitudes, locations and times, and leave out the rest of the information.
When analysts interrogate these data sets at scale, they can detect useful trends in predicting company performance.
These data sets typically require real-time capture or updates for analysis, predictive modeling, and decision making.
Presenting these data sets by organizing and categorizing in a graphical format makes it easier to achieve your goals.
These data sets are modelled, and algorithms are created to apply the model to newer images to get a satisfactory result.
These data sets currently include data from the Human Genome Project, the U.S. Census, Wikipedia, and other sources.
Because these data sets contain a large number of data to be tested, there is an increasing probability of getting false positives.
These data sets are so big that traditional data-processing software can't obtain the required information from it in an effective way.
These data sets would be the sentinels used by Parties to look for regional and global changes in environmental levels over time.
These data sets are then annotated, often by graduate students or through crowdsourcing platforms such as Amazon Mechanical Turk.
These data sets are collected from a variety of sources: sensors, climate information, and public information such as magazines, newspapers, and articles.