Machine learning (ML) is a subset of artificial intelligence (AI) that involves using algorithms and statistical models to enable computer systems to learn from data and improve performance on a ...
Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data ...
The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...
Talk to any industry insider, and they’ll tell you that the landscape of software testing is undergoing a paradigm shift that’s rendering many existing practices inadequate. The pace of software ...
Just 10 years ago, most application development testing strategies focused on unit testing for validating business logic, manual test cases to certify user experiences, and separate load testing ...
All machine learning models are bound by a critical factor: The quality of the data on which the model is trained. The challenge of data curation to improve the quality of machine learning and AI ...
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The training process for artificial intelligence (AI) algorithms is ...
In the field of machine learning, researchers tend to think that the method known as deep learning makes its best predictions when models are trained on a lot of data, like hundreds of thousands or ...
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