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Machine Learning for Engineers

- Using data to solve problems for physical systems
Af: Ryan G. McClarren Engelsk Paperback

Machine Learning for Engineers

- Using data to solve problems for physical systems
Af: Ryan G. McClarren Engelsk Paperback
Tjek vores konkurrenters priser

All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally "analog" disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers'' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,  demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.

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All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally "analog" disciplines-mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers'' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,  demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 247
ISBN-13: 9783030703905
Indbinding: Paperback
Udgave:
ISBN-10: 3030703908
Kategori: Machine learning
Udg. Dato: 23 sep 2022
Længde: 25mm
Bredde: 233mm
Højde: 155mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 23 sep 2022
Forfatter(e): Ryan G. McClarren
Forfatter(e) Ryan G. McClarren

Kategori Machine learning

ISBN-13 9783030703905

Sprog Engelsk

Indbinding Paperback

Sider 247

Udgave

Længde 25mm

Bredde 233mm

Højde 155mm

Udg. Dato 23 sep 2022

Oplagsdato 23 sep 2022

Forlag Springer Nature Switzerland AG

Kategori sammenhænge