9783031465673 - materials data science: introduction to data mining, machine learning and data-driven predictions for materials science and engineering di sandfeld, stefan (10 risultati)
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Taschenbuch. Condizione: Neu. Materials Data Science | Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering | Stefan Sandfeld | Taschenbuch | The Materials Research Society Series | xxvi | Englisch | 2025 | Springer | EAN 9783031465673 | Verantwortliche Person für die E…U: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 'fr…om 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.
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Mai 2025, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 644 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Editore: Springer Verlag GmbH, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Lingua: Inglese
Editore: Springer, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Lingua: Inglese
Editore: Springer, Springer Mai 2025, 2025
Serie: The Materials Research Society, Libro 4 di 4. Libro 4 di 4 - The Materials Research Society
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 imp…lemented '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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 644 pp. Englisch.
