Deep Learning in Physics : An Introduction

Erdmann, Martin/ Glombitza, Jonas/ Kasieczka, Gregor

Wiley-VCH Verlag GmbH（2021/01発売）

• 製本 Hardcover:ハードカバー版／ページ数 325 p.
• 言語 ENG
• 商品コード 9783527413881

Full Description

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.

Contents

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