windml.org

windML — windML 0.4 documentation

Provides an easy-to-use access to wind data sources within the Python. World, building upon numpy. As a machine learning module, it provides versatile tools for various learning tasks like time-series prediction, classification, clustering, dimensionality reduction, and related tasks. For further information including the motivation for windML, see section. For an installation guide, an overview of the architecture, and the functionalities of windML, please visit the. Windml.mapping.power mapping. Journa...

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Provides an easy-to-use access to wind data sources within the Python. World, building upon numpy. As a machine learning module, it provides versatile tools for various learning tasks like time-series prediction, classification, clustering, dimensionality reduction, and related tasks. For further information including the motivation for windML, see section. For an installation guide, an overview of the architecture, and the functionalities of windML, please visit the. Windml.mapping.power mapping. Journa...

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windML — windML 0.4 documentation | windml.org Reviews

https://windml.org

Provides an easy-to-use access to wind data sources within the Python. World, building upon numpy. As a machine learning module, it provides versatile tools for various learning tasks like time-series prediction, classification, clustering, dimensionality reduction, and related tasks. For further information including the motivation for windML, see section. For an installation guide, an overview of the architecture, and the functionalities of windML, please visit the. Windml.mapping.power mapping. Journa...

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Python Module Index — windML 0.5 documentation

http://www.windml.org/py-modindex.html

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Motivation — windML 0.4 documentation

http://www.windml.org/about.html

WindML aims at minimizing the obstacles for data-driven research in the wind power domain. Before the practitioner can start the data mining process, numerous steps have to be taken like accessing different data bases via interfaces, preprocessing, choice and parameterizations and appropriate methods. With a framework that bounds specialized mining algorithms to data sets of a particular domain, frequent steps of the data mining process chain can be re-used and simplified.

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Python Module Index — windML 0.5 documentation

http://www.windml.org/np-modindex.html

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Getting Started — windML 0.5 documentation

http://www.windml.org/gettingstarted.html

The first part of this page provides an installation guide, see. Second, an overview of the architecture is presented and the core features and components are discussed, see. At last, it is exemplary illustrated how windML is used, see. Before the installation of windML you have to make sure that all needed. Are installed. In order to install windML, you have to check out a working copy of our stable branch in our development repository. Git clone https:/ github.com/cigroup-ol/windml.git. Objects, one ca...

5

Index — windML 0.5 documentation

http://www.windml.org/genindex.html

Add measurements() (windml.model.turbine.Turbine method). Add turbine() (windml.model.windpark.Windpark method). Bytes to string() (windml.datasets.nrel.NREL method). Download with progress bar() (windml.datasets.nrel.NREL method). Fetch nrel data() (windml.datasets.nrel.NREL method). Fetch nrel meta data() (windml.datasets.nrel.NREL method). Fetch nrel meta data all() (windml.datasets.nrel.NREL method). Get features park() (windml.mapping.power diff mapping.PowerDiffMapping method). Get windpark nearest...

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madewithtea.com

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I was full-time employed as a data engineer in the field of article recommendations for online publishers. This work involved machine learning and text mining skills, as well as designing software architectures with Scala. In detail, the development of micro services for semantic analysis, classification and recommendation services. Besides, I was a product owner for Onsite Recommendations in the machine learning team and supported dev ops setting up the data infrastructure services such as RabbitMQ.

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Computational Intelligence — Uni Oldenburg

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Carl von Ossietzky Universität Oldenburg. II School of Computing Science, Business Administration, Economics, and Law. Department of Computing Science. Prospective students (in german). Learning and Cognitive Systems. Media Informatics and Multimedia Systems. Microrobotics and Control Engineering. Business Information Systems / VLBA. Learning and Cognitive Systems. Media Informatics and Multimedia Systems. Microrobotics and Control Engineering. Business Information Systems / VLBA. Die Uni bei Facebook.

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windml.org

windML — windML 0.4 documentation

Provides an easy-to-use access to wind data sources within the Python. World, building upon numpy. As a machine learning module, it provides versatile tools for various learning tasks like time-series prediction, classification, clustering, dimensionality reduction, and related tasks. For further information including the motivation for windML, see section. For an installation guide, an overview of the architecture, and the functionalities of windML, please visit the. Windml.mapping.power mapping. Journa...

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