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020 _a9783031796654
_9978-3-031-79665-4
024 7 _a10.1007/978-3-031-79665-4
_2doi
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072 7 _aTBC
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072 7 _aTEC000000
_2bisacsh
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082 0 4 _a620
_223
100 1 _aArif, Tariq M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983612
245 1 0 _aIntroduction to Deep Learning for Engineers
_h[electronic resource] :
_bUsing Python and Google Cloud Platform /
_cby Tariq M. Arif.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXV, 93 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_983616
650 0 _aEngineering design.
_93802
650 0 _aMicrotechnology.
_928219
650 0 _aMicroelectromechanical systems.
_96063
650 1 4 _aTechnology and Engineering.
_983623
650 2 4 _aElectrical and Electronic Engineering.
_983625
650 2 4 _aEngineering Design.
_93802
650 2 4 _aMicrosystems and MEMS.
_983628
710 2 _aSpringerLink (Online service)
_983630
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031796661
776 0 8 _iPrinted edition:
_z9783031796647
776 0 8 _iPrinted edition:
_z9783031796678
830 0 _aSynthesis Lectures on Mechanical Engineering,
_x2573-3176
_983631
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79665-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c85537
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