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LUMIN - a deep learning and data science ecosystem for high-energy physics

<!--HTML-->[LUMIN][1] aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to h...

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Autor principal: Strong, Giles Chatham
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2672119
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author Strong, Giles Chatham
author_facet Strong, Giles Chatham
author_sort Strong, Giles Chatham
collection CERN
description <!--HTML-->[LUMIN][1] aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to handle domain-specific requirements and problems. It also intends to provide easy access to to state-of-the-art methods, but still be flexible enough for users to inherit from base classes and override methods to meet their own demands. In this talk I will be introducing the library, discussing some of its distinguishing characteristics, and going through an example workflow. There will also be a general invitation for people to test out the library and provide feedback, suggestions, or contributions. [1]: https://github.com/GilesStrong/lumin
id cern-2672119
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
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spelling cern-26721192022-11-02T22:33:37Zhttp://cds.cern.ch/record/2672119engStrong, Giles ChathamLUMIN - a deep learning and data science ecosystem for high-energy physics3rd IML Machine Learning WorkshopLPCC Workshops<!--HTML-->[LUMIN][1] aims to become a deep-learning and data-analysis ecosystem for High-Energy Physics, and perhaps other scientific domains in the future. Similar to Keras and fastai it is a wrapper framework for a graph computation library (PyTorch), but includes many useful functions to handle domain-specific requirements and problems. It also intends to provide easy access to to state-of-the-art methods, but still be flexible enough for users to inherit from base classes and override methods to meet their own demands. In this talk I will be introducing the library, discussing some of its distinguishing characteristics, and going through an example workflow. There will also be a general invitation for people to test out the library and provide feedback, suggestions, or contributions. [1]: https://github.com/GilesStrong/luminoai:cds.cern.ch:26721192019
spellingShingle LPCC Workshops
Strong, Giles Chatham
LUMIN - a deep learning and data science ecosystem for high-energy physics
title LUMIN - a deep learning and data science ecosystem for high-energy physics
title_full LUMIN - a deep learning and data science ecosystem for high-energy physics
title_fullStr LUMIN - a deep learning and data science ecosystem for high-energy physics
title_full_unstemmed LUMIN - a deep learning and data science ecosystem for high-energy physics
title_short LUMIN - a deep learning and data science ecosystem for high-energy physics
title_sort lumin - a deep learning and data science ecosystem for high-energy physics
topic LPCC Workshops
url http://cds.cern.ch/record/2672119
work_keys_str_mv AT stronggileschatham luminadeeplearninganddatascienceecosystemforhighenergyphysics
AT stronggileschatham 3rdimlmachinelearningworkshop