LEADER 03394nam 2200457 450 001 996464380903316 005 20210330113745.0 010 $a3-030-61081-0 024 7 $a10.1007/978-3-030-61081-4 035 $a(CKB)4100000011631391 035 $a(DE-He213)978-3-030-61081-4 035 $a(MiAaPQ)EBC6455969 035 $a(PPN)252515161 035 $a(EXLCZ)994100000011631391 100 $a20210330d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational methods for deep learning $etheoretic, practice and applications /$fWei Qi Yan 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XVII, 134 p. 23 illus., 22 illus. in color.) 225 1 $aTexts in Computer Science,$x1868-0941 311 $a3-030-61080-2 327 $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 $aIntegrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security. . 410 0$aTexts in Computer Science,$x1868-0941 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aYan$b Wei Qi$0994732 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464380903316 996 $aComputational methods for deep learning$92814551 997 $aUNISA