國立虎尾科技大學 |

Machine Learning for Engineers = Using data to solve problems for physical systems /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning for Engineers/ by Ryan G. McClarren.
其他題名:	Using data to solve problems for physical systems /
作者:	McClarren, Ryan G.
面頁冊數:	XIII, 247 p. 106 illus., 90 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Nuclear Energy. -
電子資源:	https://doi.org/10.1007/978-3-030-70388-2
ISBN:	9783030703882

Machine Learning for Engineers = Using data to solve problems for physical systems /
McClarren, Ryan G.

Machine Learning for EngineersUsing data to solve problems for physical systems /[electronic resource] :by Ryan G. McClarren. - 1st ed. 2021. - XIII, 247 p. 106 illus., 90 illus. in color.online resource.

Part I Fundamentals -- 1. Introduction -- 2. The landscape of machine learning -- 3. Linear models -- 4. Tree-based models -- 5. Clustering data -- Part II Deep Neural Networks -- 6. Feed-forward Neural networks -- 7.convolutional neural networks -- 8. Recurrent neural networks for time series data -- Part III Advanced topics in machine learning -- 9. Unsupervised learning with neural networks -- 10. Reinforcement learning -- 11. Transfer learning -- Part IV Appendixes -- Appendix A. Sci-Kit learn -- Appendix B. Tensorflow.

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.

ISBN: 9783030703882

Standard No.: 10.1007/978-3-030-70388-2doiSubjects--Topical Terms:

883468
Nuclear Energy.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Machine Learning for Engineers = Using data to solve problems for physical systems /
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