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020			_a9783030703882
024	7		_a10.1007/978-3-030-70388-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347.M33 _b2021 EB
100	1		_aMcClarren, Ryan G. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681781
245	1	0	_aMachine Learning for Engineers : _bUsing data to solve problems for physical systems _cby Ryan G. McClarren.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XIII, 247 páginas) _b106 ilustraciones, 90 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_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.
520	3		_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.
988			_aSpringer_Robotics_2021
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783030703875
776	0	8	_iPrinted edition: _z9783030703899
776	0	8	_iPrinted edition: _z9783030703905
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-70388-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE