About
[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.
0:00:00 Slide 1
0:00:26 Slide 2
0:00:38 Slide 3
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The talk
Credits and licence
- Institution
- CERN
- Department
- LPCC Workshops
- Speaker
- Giles Chatham Strong
- Year
- 2019
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed