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Machine Learning for Engineers - Using data to solve problems for physical systems (Hardcover, 1st ed. 2021) Loot Price: R2,400
Discovery Miles 24 000
Machine Learning for Engineers - Using data to solve problems for physical systems (Hardcover, 1st ed. 2021): Ryan G. McClarren

Machine Learning for Engineers - Using data to solve problems for physical systems (Hardcover, 1st ed. 2021)

Ryan G. McClarren

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Loot Price R2,400 Discovery Miles 24 000 | Repayment Terms: R225 pm x 12*

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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.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Release date: September 2021
First published: 2021
Authors: Ryan G. McClarren
Dimensions: 235 x 155 x 22mm (L x W x T)
Format: Hardcover
Pages: 247
Edition: 1st ed. 2021
ISBN-13: 978-3-03-070387-5
Categories: Books > Professional & Technical > Technology: general issues > Engineering: general
Books > Professional & Technical > Energy technology & engineering > Nuclear power & engineering
Books > Professional & Technical > Civil engineering, surveying & building > General
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
Books > Professional & Technical > Mechanical engineering & materials > Mechanical engineering > General
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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LSN: 3-03-070387-8
Barcode: 9783030703875

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