bsm4tops-gnn
  • BSM4tops machine learning project
  • Introduction
  • Problem description
  • Concepts
    • Exotic heavy particles in four-top final states
    • Artificial neural networks
    • Graph neural networks
  • Hands-on: simple problem
    • Diving into python
    • Simulation of a four-top-quark process
    • Exploring the dataset
    • Creating a classifier
    • Artificial neural networks for classification
    • Graph neural networks for classification
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  1. Concepts

Artificial neural networks

PreviousExotic heavy particles in four-top final statesNextGraph neural networks

Last updated 3 years ago

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Book about machine learning in physics

A very nice and gentle introduction (based on TensorFlow and Keras) is provided in the textbook Deep Learning for Physics Research by , , , and Uwe Klemradt.

Perhaps your library has a copy of the book. The Kindle eBook is at 35$.

You can find the excercises to the book (for free) here:

Introduction to Machine Learning

This video provides an introduction into the history of machine learning. You don't need it for tackling the problem. It gives some perspective and aims for a broader picture.

To get some perspective in what areas of particle physics machine learning is used, please have a look at this review article about the use of machine learning in LHC physics:

Building intuition about deep learning

A very nice introduction to build up intuition what deep learning actually is: an optimisation problem in which arbitrary (unknown) functions are approximated is the YouTube series by 3Blue1Brown.

Introduction to PyTorch

Alfredo Canziani (the same guy from the introduction video) provides a very nice introduction into PyTorch.

You can have a look at this GitHub project:

The tutorial makes heavy use of Jupyter notebooks. If you want to try them, you can open them directly in your browser, using a service called binder. Try it out:

The suggested tutorial notebooks are:

A different introductio to PyTorch is offered here:

You can also open this notebook interactively (this time not via binder but via Google's Colab):

https://github.com/Atcold/pytorch-Deep-Learning/blob/master/01-tensor_tutorial.ipynb
https://github.com/Atcold/pytorch-Deep-Learning/blob/master/03-autograd_tutorial.ipynb
https://github.com/Atcold/pytorch-Deep-Learning/blob/master/04-spiral_classification.ipynb
Martin Erdmann
Jonas Glombitza
Gregor Kasieczka
LogoDeep Learning for Physics Research
InformationDeep Learning for Physics Research
LogoDeep Learning and its Application to LHC PhysicsarXiv.org
Logo3Blue1Brown
LogoGitHub - Atcold/pytorch-Deep-Learning: Deep Learning (with PyTorch)GitHub
LogoGitHub: Atcold/pytorch-Deep-Learning/master
Logomlhep2019/seminar_pytorch.ipynb at master · yandexdataschool/mlhep2019GitHub
Logomlhep2019/seminar_pytorch.ipynb at master · yandexdataschool/mlhep2019GitHub