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008			210817s2021 si | s |||| 0|eng d
020			_a9789811622335
024	7		_a10.1007/978-981-16-2233-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aChen, Lei _eautor _9681248
245	1	0	_aDeep Learning and Practice with MindSpore _cby Lei Chen
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 394 páginas) _b357 ilustraciones, 13 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aCognitive Intelligence and Robotics _x2520-1964
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aChapter 1. Introduction -- Chapter 2. Deep Learning Basics -- Chapter 3. DNN -- Chapter 4. Training of DNNs -- Chapter 5. Convolutional Neural Network -- Chapter 6. RNN -- Chapter 7. Unsupervised Learning: Word Vector -- Chapter 8. Unsupervised Learning: Graph Vector -- Chapter 9. Unsupervised Learning: Deep Generative Model -- Chapter 10. Deep Reinforcement Learning -- Chapter 11. Automated Machine Learning -- Chapter 12. Device-Cloud Collaboration -- Chapter 13. Deep Learning Visualization -- Chapter 14. Data Preparation for Deep Learning.
520	3		_aThis book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-2233-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eu _zSI