SSEC Unidata Server Has Been Shut Down!

The NSF Unidata server hosted at the Space Science and Engineering Center (SSEC) at the University of Wisconsin, Madison (unidata3.ssec.wisc.edu) has been permanently decommissioned.

Those who used the services provided by unidata3.ssec.wisc.edu should switch to the following alternate servers:

Service If you used Switch to
McIDAS ADDE adde.ssec.wisc.edu adde.ucar.edu
IDD/LDM Upstream idd.ssec.wisc.edu idd.unidata.ucar.edu
HTTP access to IDD Data atm.ucar.edu

Please reach out to us at support@unidata.ucar.edu if you have any questions!

Why is the Keras 3 Release a Big Deal for the Deep Learning Community?

Chart depicting use of different machine learning frameworks

The Keras package is an open-source library that provides a Python interface for deep learning. Keras is intended to be a user-friendly, modular, and extensible way to enable fast experimentation with deep neural networks. With Keras version 3, the package provides APIs for using three backends: TensorFlow, Jax, and PyTorch.

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SSEC Unidata Server Shutting Down in April 2024

The NSF Unidata server hosted at the Space Science and Engineering Center (SSEC) at the University of Wisconsin, Madison (unidata3.ssec.wisc.edu) will be permanently decomissioned on April 26, 2024.

Those using services provided by unidata3.ssec.wisc.edu can switch to using the servers described in this article.

[Read More]

Shutdown of Two Special-Purpose THREDDS Data Servers

NSF Unidata will be shutting down two existing special-purpose THREDDS Data Servers on April 15, 2024. Please read the full article for details.

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K Nearest Neighbors

Fred Rogers

K Nearest Neighbors (KNN) is a supervised machine learning method that 'memorizes' (stores) an entire dataset, then relies on the concepts of proximity and similarity to make predictions about new data. The basic idea is that if a new data point is in some sense 'close' to existing data points, its value is likely to be similar to the values of its neighbors. In the Earth Systems Sciences, such techniques can be useful for small- to moderate-scale classification and regression problems.

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News and information from the Unidata Program Center
News@Unidata
News and information from the Unidata Program Center

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