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010   $a981-9948-23-1
024 7 $a10.1007/978-981-99-4823-9
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035   $a(Au-PeEL)EBL30745830
035   $a(CKB)28234559200041
035   $a(OCoLC)1398313401
035   $a(DE-He213)978-981-99-4823-9
035   $a(EXLCZ)9928234559200041
100   $a20230915d2023      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aComputational Methods for Deep Learning $eTheory, Algorithms, and Implementations /$fby Wei Qi Yan
205   $a2nd ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (235 pages)
225 1 $aTexts in Computer Science,$x1868-095X
300   $aIncludes index.
311 08$aPrint version: Yan, Wei Qi Computational Methods for Deep Learning Singapore : Springer,c2023 9789819948222 
327   $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.
330   $aThe first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
410  0$aTexts in Computer Science,$x1868-095X
606   $aMachine learning
606   $aNeural networks (Computer science)
606   $aComputer science$xMathematics
606   $aImage processing$xDigital techniques
606   $aComputer vision
606   $aArtificial intelligence
606   $aMachine Learning
606   $aMathematical Models of Cognitive Processes and Neural Networks
606   $aMathematics of Computing
606   $aComputer Imaging, Vision, Pattern Recognition and Graphics
606   $aArtificial Intelligence
615  0$aMachine learning.
615  0$aNeural networks (Computer science)
615  0$aComputer science$xMathematics.
615  0$aImage processing$xDigital techniques.
615  0$aComputer vision.
615  0$aArtificial intelligence.
615 14$aMachine Learning.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
615 24$aMathematics of Computing.
615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics.
615 24$aArtificial Intelligence.
676   $a005.7
700   $aYan$b Wei Qi$0994732
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910746070603321
996   $aComputational methods for deep learning$92814551
997   $aUNINA