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Author(s)

NameInstitutionMail AddressSocial Contacts
Brunella D'AnziINFN Sezione di Bari brunella.d'anzi@cern.chSkype: live:ary.d.anzi_1; Linkedin: brunella-d-anzi
Nicola De FilippisINFN Sezione di Bari nicola.defilippis@ba.infn.itN/A

Domenico Diacono

INFN Sezione di Bari

domenico.diacono@ba.infn.itN/A
Walaa ElmetenaweeINFN Sezione di Bariwalaa.elmetenawee@cern.chN/A
Giorgia MinielloINFN Sezione di Barigiorgia.miniello@ba.infn.itN/A
Andre SznajderRio de Janeiro State Universitysznajder.andre@gmail.comN/A

How to Obtain Support

General Information

ML/DL TechnologiesDeep Neural Networks (DNN), Random Forest (RF)
Science FieldsHigh Energy Physics
DifficultyLow
Language

English

Type

fully annotated and runnable

Software and Tools

Programming LanguagePython
ML Toolset

Tensorflow, Keras, Scikit-learn 

Additional librariesuproot, NumPy, pandas,h5py,seaborn,matplotlib
Suggested EnvironmentsGoogle's Colaboratory

Needed datasets

Data CreatorLHC Experiment
Data TypeSimulation
Data Size2 GB
Data SourceCloud@ReCaS-Bari

Short Description of the Use Case

How to execute it

Use Googe Colab 

Google's Colaboratory is a free online cloud-based Jupyter notebook environment on Google-hosted machines, with some added features, like the possibility to attach a GPU or a TPU if needed with 12 hours of continuous execution time. After that, the whole virtual machine is cleared and one has to start again. The user can run multiple CPU,GPU, and TPU instances simultaneously, but the resources are shared between these instances.

The notebook for this tutorial can be found here. The .ipynb file is also available in the attachment section and in this GitHub repository.

The data set files are on the Recas Bari's ownCloud and are automatically loaded by the notebook. In case, they are also available here and here .

In the following, the most important excerpts are described.


Annotated Description

References

Attachments 

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