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Taschenbuch. Zustand: Neu. Materials Data Science | Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering | Stefan Sandfeld | Taschenbuch | xxvi | Englisch | 2025 | Springer | EAN 9783031465673 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Zustand: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2025
ISBN 10: 3031465679 ISBN 13: 9783031465673
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Sprache: Englisch
Verlag: Springer International Publishing Mai 2024, 2024
ISBN 10: 3031465644 ISBN 13: 9783031465642
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Buch. Zustand: Neu. Neuware - This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented 'from scratch' using Python and NumPy.The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes' theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced.The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a 'black box'. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented 'from scratch' using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.
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In den WarenkorbHardback. Zustand: New. New copy - Usually dispatched within 4 working days.
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In den WarenkorbHardcover. Zustand: Brand New. 320 pages. 9.19x6.13x0.71 inches. In Stock.
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Verlag: China Water Power Press Pub. Date :2008-05, 2000
ISBN 10: 7508454057 ISBN 13: 9787508454054
Anbieter: liu xing, Nanjing, JS, China
paperback. Zustand: New. Paperback. Pub Date: 2008 Pages: 280 Language: Chinese in Publisher: China Water Power Press University computer based on machine guidance and problem sets are compiled by Liu Yung. Du Xiaodan. Yan Tao School of Computer Basics (ISBN978.7.5084.5396-5) on supporting the use of machine guidance and problem sets. University computer based on machine guidance and problem sets include: basic operations with common computer hardware assembly and maintenance. Windows XP. Word2003. Excel2003. PowerPo.