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001439865 001__ 1439865
001439865 003__ OCoLC
001439865 005__ 20230309004526.0
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001439865 019__ $$a1272996406$$a1284933920
001439865 020__ $$a9783030703882$$q(electronic bk.)
001439865 020__ $$a3030703886$$q(electronic bk.)
001439865 020__ $$z9783030703875
001439865 020__ $$z3030703878
001439865 0247_ $$a10.1007/978-3-030-70388-2$$2doi
001439865 035__ $$aSP(OCoLC)1269054727
001439865 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001439865 049__ $$aISEA
001439865 050_4 $$aTA347.M33$$bM33 2021
001439865 08204 $$a620.00285/63$$223
001439865 1001_ $$aMcClarren, Ryan G.,$$eauthor.
001439865 24510 $$aMachine learning for engineers :$$busing data to solve problems for physical systems /$$cRyan G. McClarren.
001439865 264_1 $$aCham :$$bSpringer,$$c[2021]
001439865 264_4 $$c©2021
001439865 300__ $$a1 online resource :$$billustrations (chiefly color)
001439865 336__ $$atext$$btxt$$2rdacontent
001439865 337__ $$acomputer$$bc$$2rdamedia
001439865 338__ $$aonline resource$$bcr$$2rdacarrier
001439865 504__ $$aIncludes bibliographical references and index.
001439865 5050_ $$aPart 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.
001439865 506__ $$aAccess limited to authorized users.
001439865 520__ $$aAll 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
001439865 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 8, 2021).
001439865 650_0 $$aMachine learning.
001439865 650_0 $$aArtificial intelligence$$xEngineering applications.
001439865 650_6 $$aApprentissage automatique.
001439865 650_6 $$aIntelligence artificielle$$xApplications en ingénierie.
001439865 655_0 $$aElectronic books.
001439865 77608 $$iPrint version:$$aMcClarren, Ryan G.$$tMachine learning for engineers.$$dCham : Springer, [2021]$$z3030703878$$z9783030703875$$w(OCoLC)1235415836
001439865 852__ $$bebk
001439865 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-70388-2$$zOnline Access$$91397441.1
001439865 909CO $$ooai:library.usi.edu:1439865$$pGLOBAL_SET
001439865 980__ $$aBIB
001439865 980__ $$aEBOOK
001439865 982__ $$aEbook
001439865 983__ $$aOnline
001439865 994__ $$a92$$bISE