Exact(32)
Limitations of I/O bandwidth and latency are a serious burden for many data intensive algorithms.
Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems.
For many data collections, e.g. lists of molecular structures with their associated identifiers (name, link, database ID, etc).
For many data centers, the ratio of incoming energy to IT energy is 2-to-1, or even worse.
Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications.
Feature selection is a task of fundamental importance for many data mining or machine learning applications, including regression.
Similar(28)
For many data-poor countries however, global datasets such as these are the only consistent estimates of biodiversity that are available.
While the UK is one of only three countries with mandatory data submission for national audit, and the data completeness and quality were very high for many data-fields, there are inevitable problems in using an audit database for research and then using that research for audit.
The approach could also be useful for meta-analysis of many data sets from different sources.
Enterprises like to say they're data driven, but for many that data is a mess spread across the organization.
Hadoop, famously named after a toy elephant, is a well known piece of software to anyone curious about data science and it provides the backbone for many big data systems, allowing businesses to store and analyse masses of data.
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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