Suchergebnisse

Front cover -- Half title -- Title -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1 Machine learning methods -- 1.1 Introduction -- 1.2 Holistic view of learning models -- 1.2.1 Supervised learning -- 1.2.2 Unsupervised learning -- 1.2.3 Hybrid learning -- 1.2.4 Reinforcement learning -- 1.3 Classification of learning techniques -- 1.3.1 Data perspective -- 1.3.2 Algorithmic perspective -- 1.4 Machine learning methods -- 1.4.1 Dimensionality reduction -- 1.4.2 Neural networks and deep learning -- 1.4.3 Natural language processing -- 1.4.4 Machine learning as an interpolation function -- 1.5 Conclusion -- Acknowledgment -- References -- Chapter 2 Learning first-principles knowledge from data -- 2.1 Background -- 2.2 Approaches to analyze manufacturing data -- 2.2.1 Static data approaches -- 2.2.2 Dynamic data approaches -- 2.3 Automation of model selection and hyperparameter search -- 2.3.1 Automation for general data: AutoML -- 2.3.2 Automation for manufacturing data: smart process data analytics -- 2.4 Conclusion -- References -- Chapter 3 Convolutional neural networks: Basic concepts and applications in manufacturing -- 3.1 Introduction -- 3.2 Data objects and mathematical representations -- 3.2.1 Tensor representations -- 3.2.2 Graph representations -- 3.2.3 Color representations -- 3.3 Convolutional neural network architectures -- 3.3.1 Convolution operations -- 3.3.2 Activation functions -- 3.3.3 Pooling -- 3.3.4 Convolution blocks -- 3.3.5 Feedforward neural networks -- 3.3.6 Data augmentation -- 3.3.7 Training and testing procedures -- 3.3.8 CNN architecture optimization -- 3.3.9 Transfer learning -- 3.4 Case studies -- 3.4.1 CNNs for sensor design -- 3.4.2 Molecule design -- 3.4.3 Decoding of spectra -- 3.4.4 CNNs for multivariate process monitoring -- 3.4.5 CNNs for image-based feedback control -- 3.5 Conclusion.