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