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This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks. ![]() English | 2022 | ISBN: 1399400592 | 241 pages | True PDF EPUB | 10.81 MB About this item ![]() English | 2022 | ISBN: 1009123238, 978-1009123235 | 411 pages | PDF | 10.31 MB This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. 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