000 02219cam a22002538i 4500
001 10604
003 IN-BhIIT
005 20231229154844.0
008 220303s2022 enk b 001 0 eng
020 _a9783030703905
040 _aIN-BhIIT
041 _aeng
082 0 0 _a006.31
_bMCC/M
100 1 _aMcClarren, Ryan G.
_eAuthor.
_921270
245 1 0 _aMachine learning for engineers :
_busing data to solve problems for physical systems /
_cby Ryan G. McClarren.
260 _aSwitzerland :
_bSpringer,
_c2021.
300 _axii, 247 p. :
_bill. ;
_c24 cm
504 _aIncludes bibliographical references and index.
520 _a" 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. "
650 0 _aEngineering
_xData processing.
_921271
650 0 _aMachine learning.
650 7 _aTechnology & engineering
_xSignals & Signal Processing.
_921272
942 _cTRB
_01
999 _c13361
_d13361