The Phi-4 model was trained on just 1.4 million carefully chosen prompt-response pairs. Instead of brute force, the Microsoft ...
The purpose of this course is to share the methods, models and practices that can be applied within data science, to ensure that the data used in problem-solving is relevant and properly manipulated ...
ZURICH--(BUSINESS WIRE)--RepRisk, a leading ESG data science firm combining machine learning and human intelligence to identify ESG risks, takes a major step forward in enabling more sustainable ...
Mathematics professor Andrea Cullinen, computer science instructor Nathalie Guebels and geography professor Geordie Armstrong ...
This program trains the next generation of geographic data and quantitative social scientists who are interested in understanding how to use data science and other quantitative methods to tackle ...
We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of ...
Data science is an exciting and rapidly growing field that involves extracting insights and knowledge from data. To land a top data science job, it is important to have a solid foundation in key data ...
Researchers have developed a new method -- 'Pixel Approximate Entropy' -- that measures the complexity of a data visualization and can be used to develop easier to read visualizations. 'In fast-paced ...
This paper, compiled by Prof. Huadong Guo and his team, discusses the potential and utility of Big Earth Data through a number of case studies to support the 2030 Agenda for Sustainable Development.
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