Stefan sandfeld (19 resultados)
Idioma: Inglés
Editorial: Springer, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Condición: New. 2024th edition NO-PA16APR2015-KAP.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 99,22
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 96,20
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 98,30
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Condición: New.
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer, 2023
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: moluna, Greven, Alemaniamoluna
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EUR 81,44
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Condición: New.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 63,85
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Taschenbuch. Condición: 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 EU…: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 76,37
Envío por EUR 64,81Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 'fro…m 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.
Idioma: Inglés
Editorial: Springer International Publishing Mai 2024, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 96,29
Envío por EUR 66,19Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: 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.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 58,23
Envío por EUR 5,50Se envía de Italia a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 73,30
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Condición: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
Idioma: Inglés
Editorial: Springer, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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- Impresión bajo demanda
Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 78,24
Envío por EUR 11,00Se envía de Italia a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 92,63
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Condición: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
Idioma: Inglés
Editorial: Springer International Publishing, Springer International Publishing Mai 2025, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 69,54
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 644 pp. Englisch.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 98,61
Envío por EUR 7,62Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
Idioma: Inglés
Editorial: Springer Verlag GmbH, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: moluna, Greven, Alemaniamoluna
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EUR 60,06
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Idioma: Inglés
Editorial: Springer, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
- Tapa blanda
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 101,07
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Condición: New. PRINT ON DEMAND.
Idioma: Inglés
Editorial: Springer International Publishing, Springer Nature Switzerland Mai 2024, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 96,29
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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 impleme…nted '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. 644 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Springer Mai 2025, 2025
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 69,54
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: 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 impl…emented '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.
Idioma: Inglés
Editorial: Springer, Springer Mai 2024, 2024
Serie: The Materials Research Society, Libro 4 de 4. Libro 4 de 4 - The Materials Research Society
- Tapa dura
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 96,29
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Buch. Condición: 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 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 644 pp. Englisch.
