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Table of Contents


NameInstitutionMail AddressSocial Contacts
Lucio AnderliniINFN Sezione di FirenzeLucio.Anderlini@fi.infn.itHangouts:
Matteo TurisiniUniversità La SapienzaMatteo.Turisini@roma1.infn.itN/A

How to Obtain Support

General Information

ML/DL TechnologiesForward Neural Networks; Deployment in C
Science FieldsGeneric
Typefully annotated

Software and Tools

Programming LanguagePython, C
ML ToolsetKeras + Tensorflow 1.x
Additional librariesscikit-learn
Suggested EnvironmentsINFN-Cloud VM, bare Linux Node, Google CoLab

Needed datasets

Data Creatorscikit-learn community
Data Typeimage
Data Size< 1 GB
Data Sourcescikit-learn package

Short Description of the Use Case

In the life of a physicist, the time arrives when you get disgusted by the software dependencies that evaluating a simple neural network requires and you want the plain, pure and clean function to be evaluated in a whatever context. 


The other reason is that the original data that we used to train this are not public. 

How to execute it

Download and run the jupyter notebook: