LEADER 05569nam 22006015 450 001 9910760286403321 005 20251016152318.0 010 $z9783031454677$bhardback 010 $a9783031454684$bebook 010 $a3031454685 024 7 $a10.1007/978-3-031-45468-4 035 $a(MiAaPQ)EBC30853138 035 $a(Au-PeEL)EBL30853138 035 $a(DE-He213)978-3-031-45468-4 035 $a(CKB)28652798700041 035 $a(EXLCZ)9928652798700041 100 $a20231101d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep learning $efoundations and concepts /$fby Christopher M. Bishop, Hugh Bishop 210 1$aCham :$cSpringer,$d[2024]. 215 $a1 online resource (656 pages) 311 08$aPrint version: Bishop, Christopher M. Deep Learning Cham : Springer International Publishing AG,c2023 9783031454677 320 $aIncludes bibliographical references and index. 327 $aPreface -- The Deep Learning Revolution -- Probabilities -- Standard Distributions -- Single-layer Networks: Regression -- Single-layer Networks: Classification -- Deep Neural Networks -- Gradient Descent -- Backpropagation -- Regularization -- Convolutional Networks -- Structured Distributions -- Transformers -- Graph Neural Networks -- Sampling -- Discrete Latent Variables -- Continuous Latent Variables -- Generative Adversarial Networks -- Normalizing Flows -- Autoencoders -- Diffusion Models -- Appendix A Linear Algebra -- Appendix B Calculus of Variations -- Appendix C Lagrange Multipliers -- Biblyography -- Index. 330 $aThis book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. ?Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.? -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." ? Yann LeCun ?This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring inprobability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.? -- Yoshua Bengio. 606 $aArtificial intelligence 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aData Science 606 $aAprenentatge profund (Aprenentatge automātic)$2lemac 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aData Science 615 7$aAprenentatge profund (Aprenentatge automātic) 676 $a733 700 $aBishop$b Christopher M.$061568 702 $aBishop$b Hugh 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760286403321 996 $aDeep Learning$93598198 997 $aUNINA