000 | 03404nam a22005895i 4500 | ||
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001 | 978-981-99-4823-9 | ||
003 | DE-He213 | ||
005 | 20240423130110.0 | ||
007 | cr nn 008mamaa | ||
008 | 230915s2023 si | s |||| 0|eng d | ||
020 |
_a9789819948239 _9978-981-99-4823-9 |
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024 | 7 |
_a10.1007/978-981-99-4823-9 _2doi |
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050 | 4 | _aQ325.5-.7 | |
072 | 7 |
_aUYQM _2bicssc |
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_aMAT029000 _2bisacsh |
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_aUYQM _2thema |
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082 | 0 | 4 |
_a006.31 _223 |
100 | 1 |
_aYan, Wei Qi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aComputational Methods for Deep Learning _h[electronic resource] : _bTheory, Algorithms, and Implementations / _cby Wei Qi Yan. |
250 | _a2nd ed. 2023. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023. |
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300 |
_aXX, 222 p. 40 illus., 36 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aTexts in Computer Science, _x1868-095X |
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505 | 0 | _a1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning. | |
520 | _aThe first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas. | ||
650 | 0 | _aMachine learning. | |
650 | 0 | _aNeural networks (Computer science) . | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aMathematical Models of Cognitive Processes and Neural Networks. |
650 | 2 | 4 | _aMathematics of Computing. |
650 | 2 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
650 | 2 | 4 | _aArtificial Intelligence. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789819948222 |
776 | 0 | 8 |
_iPrinted edition: _z9789819948246 |
830 | 0 |
_aTexts in Computer Science, _x1868-095X |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-99-4823-9 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c185035 _d185035 |