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Deep Learning in Physics. An Introduction. Edition No. 1

  • ID: 5185806
  • Book
  • January 2021
  • 325 Pages
  • John Wiley and Sons Ltd
The book introduces the reader to Deep Learning, an advanced machine learning method to analyze data and find patterns by means of self-adapting, self-improving neural networks. After an overview of the fundamentals, the book explains the different network architectures used in Deep Learning and the different learning methods such as energy-driven, reductionist and success target learning. The last part deals with the advanced concepts of Deep Learning such as weak and unsupervised training and hybrid network architectures.
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PART I: BASIC UNDERSTANDING
Relevance of Machine Learning
Basic Idea of Deep Learning
Neural Networks as Multivariate, Multidimensional Models
Optimization of Network Parameters -
Quality of Modelling

PART II: NETWORK ARCHITECTURES
Basic Architecture and Extensions
Analysis of Image Data
Analysis of Point Clouds
Time Series and Variable Input Data
Learning with Success Targets
Energy-Driven Learning Methods
Reduction to Essential Information
Cooperation of Several Networks

PART III: NETWORK INSIGHTS AND ADVANCED CONCEPTS
Understanding of Trained Networks
Systematic Uncertainties
Weak and Unsupervised Training
Hybrid Architectures
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Martin Erdmann
Jonas Glombitza
Gregor Kasieczka
Uwe Klemradt
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