LEADER 04521nam 22007335 450 001 9910734836503321 005 20240619102405.0 010 $a3-031-29642-7 024 7 $a10.1007/978-3-031-29642-0 035 $a(CKB)27298744100041 035 $a(MiAaPQ)EBC30620507 035 $a(DE-He213)978-3-031-29642-0 035 $a(PPN)272259241 035 $a(MiAaPQ)EBC30612989 035 $a(Au-PeEL)EBL30612989 035 $a(EXLCZ)9927298744100041 100 $a20230629d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeural Networks and Deep Learning $eA Textbook /$fby Charu C. Aggarwal 205 $a2nd ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (xxiv, 529 pages) $cillustrations 311 $a9783031296413 320 $aIncludes bibliographical references and index. 327 $aAn Introduction to Neural Networks -- The Backpropagation Algorithm -- Machine Learning with Shallow Neural Networks -- Deep Learning: Principles and Training Algorithms -- Teaching a Deep Neural Network to Generalize -- Radial Basis Function Networks -- Restricted Boltzmann Machines -- Recurrent Neural Networks -- Convolutional Neural Networks -- Graph Neural Networks -- Deep Reinforcement Learning -- Advanced Topics in Deep Learning. 330 $aThis book covers both classical and modern models in deep learning. The chapters of this book span three categories: 1. The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2. Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. 2. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. 3. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12. The book is written for graduate students, researchers, and practitioners. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition. Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models. 606 $aMachine learning 606 $aData mining 606 $aArtificial intelligence 606 $aExpert systems (Computer science) 606 $aNatural language processing (Computer science) 606 $aMachine Learning 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 606 $aKnowledge Based Systems 606 $aNatural Language Processing (NLP) 606 $aXarxes neuronals (Informātica)$2thub 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aExpert systems (Computer science). 615 0$aNatural language processing (Computer science). 615 14$aMachine Learning. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aKnowledge Based Systems. 615 24$aNatural Language Processing (NLP). 615 7$aXarxes neuronals (Informātica) 615 7$aAprenentatge automātic 676 $a006.32 700 $aAggarwal$b Charu C.$0518673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734836503321 996 $aNeural networks and deep learning$91904942 997 $aUNINA