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008			220601s2020 sz | o |||| 0|eng d
020			_a9783031796654
024	7		_a10.1007/978-3-031-79665-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
100	1		_aArif, Tariq M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688377
245	1	0	_aIntroduction to Deep Learning for Engineers : _bUsing Python and Google Cloud Platform _cby Tariq M. Arif
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XV, 93 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Mechanical Engineering _x2573-3176
505	0		_aPreface -- Acknowledgments -- Introduction: Python and Array Operations -- Introduction to PyTorch -- Introduction to Deep Learning -- Deep Transfer Learning -- Case Study: Practical Implementation Through Transfer Learning -- Bibliography -- Author's Biography .
520			_aThis book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model. In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case. The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031796661
776	0	8	_iPrinted edition: _z9783031796647
776	0	8	_iPrinted edition: _z9783031796678
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79665-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI