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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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Lenguaje: | eng |
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2019
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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 |
record_format | invenio |
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 |