Artificial neural networks

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 Martin Erdmann, Jonas Glombitza, Gregor Kasieczka, and Uwe Klemradt.

Deep Learning for Physics Researchworldscientific.com

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:

InformationDeep Learning for Physics Research

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:

Deep Learning and its Application to LHC PhysicsarXiv.org

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.

3Blue1Brownwww.3blue1brown.com

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:

GitHub - Atcold/NYU-DLSP20: NYU Deep Learning Spring 2020GitHub

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:

GitHub: Atcold/pytorch-Deep-Learning/mastermybinder.org

The suggested tutorial notebooks are:

1. https://github.com/Atcold/pytorch-Deep-Learning/blob/master/01-tensor_tutorial.ipynb

2. https://github.com/Atcold/pytorch-Deep-Learning/blob/master/03-autograd_tutorial.ipynb

3. https://github.com/Atcold/pytorch-Deep-Learning/blob/master/04-spiral_classification.ipynb

A different introductio to PyTorch is offered here:

mlhep2019/notebooks/day-3/seminar_pytorch.ipynb at master · yandexdataschool/mlhep2019GitHub

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

mlhep2019/notebooks/day-3/seminar_pytorch.ipynb at master · yandexdataschool/mlhep2019GitHub