Showing entries tagged [gateway]

Offer: NSF Unidata Science Gateway JupyterHub Resources Available for Spring 2024 Courses

Jupyterhub

NSF Unidata offers JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the Spring 2024 term, NSF Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to their requirements.

[Read More]

Self Organizing Maps for Earth Systems Science

Representation of Self Organizing Map
Representation of nodes in a Self Organizing Map.

A self-organizing map (SOM), sometimes known as a Kohonen map after its originator the Finnish professor Teuvo Kohonen, is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set. SOMs are a specific type of artificial neural network, but use a different training strategy compared to more traditional artificial neural networks (ANNs). SOMs can be used for clustering, dimensionality reduction, feature extraction, and classification — all of which suggest that they can be important tools for understanding large Earth Systems Science (ESS) datasets.

[Read More]

Offer: Unidata Science Gateway JupyterHub Resources Available for Fall 2023 Courses

Jupyterhub

Since 2018, Unidata has been offering JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the fall 2023 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

[Read More]

Why are GPUs Exciting for Machine Learning Research?

NVIDIA A100 graphics card

Machine Learning systems are often configured around Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). Why should this be the case, in an era when CPUs are powerful and (relatively) inexpensive? This article provides some insights into what GPUs are and why they provide advantages for certain types of computations, including some commonly used for machine learning and modeling.

[Read More]

Offer: Unidata Science Gateway JupyterHub Resources Available for Spring 2023 Courses

Jupyterhub

Since 2018, Unidata has been offering JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. In that time, nearly 850 users — mostly undergraduates in atmospheric science programs — have been able to take advantage of cloud-based resources to access pre-configured computational notebooks for learning and teaching objectives.

For the spring 2023 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class.

[Read More]
News@Unidata
News and information from the Unidata Program Center
News@Unidata
News and information from the Unidata Program Center

Welcome

FAQs

Developers’ blog

Take a poll!

What if we had an ongoing user poll in here?

Browse By Topic
Browse by Topic
« May 2024
SunMonTueWedThuFriSat
   
2
3
4
5
6
7
8
9
10
11
12
13
14
16
17
18
19
20
21
23
24
25
26
27
28
29
30
31
 
       
Today