Deep Learning in Physics

Deep Learning in Physics

An Introduction

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Wiley-VCH Verlag GmbH






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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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