https://github.com/sbaldu/neural_network_hep

Implementation of a neural network framework from scratch in C++ applied to particle physics
https://github.com/sbaldu/neural_network_hep

cpp cuda high-energy-physics neural-networks

Last synced: 3 months ago
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Implementation of a neural network framework from scratch in C++ applied to particle physics

• Host: GitHub
• URL: https://github.com/sbaldu/neural_network_hep
• Owner: sbaldu
• Created: 2023-06-02T21:21:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-12T21:50:07.000Z (11 months ago)
• Last Synced: 2024-03-13T11:53:35.893Z (11 months ago)
• Topics: cpp, cuda, high-energy-physics, neural-networks
• Language: C++
• Homepage:
• Size: 4.13 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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README

        
# Neural_network_tracking

Nowadays neural networks are widely used in many branches of physics, in particular in particle 

physics.  

The Higgs ML challenge asked to classify in signal and background a set of events simulated based on the 

ATLAS detector. This is exactly the kind of problem that could be effectively tackled using a neural 

network.  

Nowadays there are a lot libraries already written to work with neural networks. The first two that

come to mind, and also the largest ones, are Tensorflow and PyTorch, which are available in Python, C++ 

and other languages.  

These libraries are very thoroughly written and efficient, but from an accademic point of view it is

instructive, in order to really understand the functioning of neural networks, to learn how to write 

them from scratch.  

The goal of this project is to write from scratch in C++ a framework for building neural networks, test 

it with the MNIST dataset and finally use it to tackle the Higgs ML challenge.