Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series) - Tapa dura
Libro 4 de 4: The Materials Research Society
Sinopsis
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. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Prof. Dr. Stefan Sandfeld is Director of the Institute for Advanced Simulation: Materials Data Science and Informatics (IAS-9) Forschungszentrum Juelich, Germany; and Professor/Chair of Materials Data Science and Materials Informatics, RWTH Aachen University.
De la contraportada
This text covers all of the artificial intelligence, deep learning, and data science topics relevant to materials science and engineering, accompanied by numerous examples and applications. The book begins with a concise introduction to statistics and probabilities, explaining important concepts and definitions such as probability functions and distributions, sampling and data preparation, Bayes’ theorem, and statistical significance testing in the context of materials science. As such it is a useful introduction for both undergraduate and graduate students as well as a refresher for research scientists and practicing engineers. The second part is a detailed description of (statistical) machine learning and deep learning. It considers a range of supervised and unsupervised methods including multi-output regression, random forests, time series prediction, and clustering as well as a number of different deep learning networks such as convolutional neural networks, auto-encoder, or generative adversarial networks. The degree of detail and theory is such that all methods can be understood and critically discussed, and it is reinforced by extensive examples within materials science and engineering. The final part considers six complex applications and advanced topics of machine learning and data mining in materials science and engineering. A comprehensive appendix is included, summarizing the most important statistical and mathematical techniques.
- Introduces machine learning/deep learning methods in detail based on examples and data from materials science;
- Covers all theoretical foundations in an accessible manner, tailored to materials scientists and engineers;
- Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material.
"Sobre este título" puede pertenecer a otra edición de este libro.
Otras ediciones populares con el mismo título
Resultados de la búsqueda para Materials Data Science: Introduction to Data Mining,...
Imagen de archivo
Materials Data Science (eng)
Impresión bajo demandaLibrería: Brook Bookstore On Demand, Napoli, NA, Italia
Calificación del vendedor: 4 de 5 estrellas
Condición: new. Questo è un articolo print on demand. Nº de ref. del artículo: WFC8MAUHYU
Comprar nuevo
EUR 78,24
Envío por EUR 11,00
Se envía de Italia a Estados Unidos de America
Se envía de Italia a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 2024th edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26399312768
Comprar nuevo
EUR 87,99
Envío por EUR 3,39
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series)
Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Nº de ref. del artículo: 398112863
Comprar nuevo
EUR 87,50
Envío por EUR 7,46
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering (The Materials Research Society Series)
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18399312778
Comprar nuevo
EUR 88,28
Envío por EUR 9,95
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen del vendedor
Materials Data Science
Publicado por
Springer International Publishing, Springer Nature Switzerland Mai 2024, 2024
ISBN 10: 3031465644
ISBN 13: 9783031465642
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
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 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. 644 pp. Englisch. Nº de ref. del artículo: 9783031465642
Comprar nuevo
EUR 96,29
Envío por EUR 23,00
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Scientists
Publicado por
Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031465644
ISBN 13: 9783031465642
Nuevo
Tapa dura
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 1106327840
Comprar nuevo
EUR 81,44
Envío por EUR 48,99
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Materials Data Science
Publicado por
Springer Nature Switzerland, Springer International Publishing Mai 2024, 2024
ISBN 10: 3031465644
ISBN 13: 9783031465642
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 644 pp. Englisch. Nº de ref. del artículo: 9783031465642
Comprar nuevo
EUR 96,29
Envío por EUR 60,00
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 2 disponibles
Imagen del vendedor
Materials Data Science : Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering
Publicado por
Springer International Publishing Mai 2024, 2024
ISBN 10: 3031465644
ISBN 13: 9783031465642
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
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. Nº de ref. del artículo: 9783031465642
Comprar nuevo
EUR 96,29
Envío por EUR 66,19
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles