This book is an introduction to modern neural networks (deep learning), intended for particle physicists. Most particle physicists need to use machine learning for data analysis or detector studies, and the unique combination of mathematical and statistical knowledge that physicists have puts them in a position to understand the topic deeply. However, most introductions to deep learning can’t assume that their readers have this background, and advanced courses assume specialized knowledge that physics audiences may not have.
This book is “introductory” because it emphasizes the foundations of what neural networks are, how they work, why they work, and it provides practical steps to train neural networks of any topology. It does not get into the (changing) world of network topologies or designing new kinds of machine learning algorithms to fit new problems.
The material in this book was first presented at CoDaS-HEP in 2024: jpivarski-talks/2024-07-24-codas-hep-ml. I am writing it in book format, rather than simply depositing my slide PDFs and Jupyter notebooks in https://hsf-training.org/, because the original format assumes that I’ll verbally fill in the gaps. This format is good for two purposes:
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