We have restored access to the website from outside the CERN network, however access from certain worldwide locations is still being blocked (read more here).

CERN Accelerating science

Home > Jet flavor tagging with Deep Learning using Python
 
Talk
Title Jet flavor tagging with Deep Learning using Python
Video
If you experience any problem watching the video, click the download button below
Download Embed
Mp4:Medium
(1000 kbps)		High
(4000 kbps)		More..
Copy-paste this code into your page:
Copy-paste this code into your page to include both slides and lecture:
Author(s) Lanfermann, Marie (speaker) (Universite de Geneve (CH))
Corporate author(s) CERN. Geneva
Imprint 2016-05-31. - Streaming video.
Series (Developers@CERN Forum)
(2nd Developers@CERN Forum)
Lecture note on 2016-05-31T14:00:00
Subject category Developers@CERN Forum
Abstract Object tagging, e.g. jet flavor tagging is seen as a classification problem from a Machine Learning point of view. Deep neural networks with multidimensional output provide one way of approaching this problem. Besides the part that implements the resulting deep neural net in the ATLAS C++ software framework, a Python framework has been developed to connect HEP data to standard Data Science Python based libraries for Machine Learning. It makes use of HDF5, JSON and Pickle as intermediate data storage format, pandas and numpy for data handling and calculations, Keras for neural net construction and training as well as testing and matplotlib for plotting. It can be seen as an example of taking advantage of outside-HEP software developments without relying on the HEP standard ROOT.
Copyright/License © 2016-2024 CERN
Submitted by jose.benito.gonzalez@cern.ch

 


 Record created 2016-06-02, last modified 2022-11-02
Similar records


External links:
Download fulltextTalk details
Download fulltextEvent details