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.
Note: Product cover images may vary from those shown
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
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
Note: Product cover images may vary from those shown
Martin Erdmann
Jonas Glombitza
Gregor Kasieczka
Uwe Klemradt
Jonas Glombitza
Gregor Kasieczka
Uwe Klemradt
Note: Product cover images may vary from those shown
