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Information

This page contains additional material for the textbook Deep Learning for Physics Research by Martin Erdmann, Jonas Glombitza, Gregor Kasieczka, and Uwe Klemradt.

The authors can be contacted under [email protected].

For more information on the book, refer to the page by the publisher.

Exercises

Section 1 - Deep Learning Basics

Chapter 3 - Building blocks of neural networks

Chapter 4 - Optimization of network parameters

Chapter 5 - Mastering model building

Section 2 - Standard Architectures of Deep Networks

Chapter 7 - Fully-connected networks: improving the classic all-rounder

  • 7.1: Classification of magnetic phases using fully-connected networks
    Problem (Download - View), Solution (Download - View)
  • 7.2: Energy reconstruction of air showers using fully-connected networks
    Problem (Download - View), Solution (Download - View)

Chapter 8 - Convolutional neural networks and analysis of image-like data

  • 8.1: Classification of magnetic phases using convolutional networks
    Problem (Download - View), Solution (Download - View)
  • 8.2: Energy reconstruction of air showers using convolutional networks
    Problem (Download - View), Solution (Download - View)

Chapter 9 - Recurrent neural networks: time series and variable input

Chapter 10 - Graph networks and convolutions beyond Euclidean domains

  • 10.1: Signal Classification using Dynamic Graph Convolutional Neural Networks
    Problem (Download - View), Solution (Download - View)
  • (16.1: Semi-supervised node classification using graph convolutional networks)
    Problem (Download - View), Solution (Download - View)

Chapter 11 - Multi-task learning, hybrid architectures, and operational reality

Section 3 - Introspection, Uncertainties, Objectives

Section 4 - Deep Learning Advanced Concepts

Chapter 16 - Weakly-supervised classification

  • 16.1: Zachary’s karate club - semi-supervised node classification
    Problem (Download - View), Solution (Download - View)

Chapter 17 - Autoencoders: finding and compressing structures in data

Chapter 18 - Generative models: data from noise

 

Citation

@book{doi:10.1142/12294,
	  author = {Erdmann, Martin and Glombitza, Jonas and Kasieczka, Gregor and Klemradt, Uwe},
	  title = {Deep Learning for Physics Research},
	  publisher = {WORLD SCIENTIFIC},
	  year = {2021},
	  doi = {10.1142/12294},
	  address = {},
	  edition   = {},
	  URL = {http://deeplearningphysics.org},
	  eprint = {https://worldscientific.com/doi/pdf/10.1142/12294}
}

 

Errata

Please report mistakes to [email protected].

So far, no errors are known.

Usage

Note: To improve the exercise page and potentially improve and extend the scope, we measure the use of the task templates and the interest in the solutions with the tool Google Analytics.