000 05545nam a22005295i 4500
001 978-3-031-45468-4
003 DE-He213
005 20240730170429.0
007 cr nn 008mamaa
008 231101s2024 sz | s |||| 0|eng d
020 _a9783031454684
_9978-3-031-45468-4
024 7 _a10.1007/978-3-031-45468-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBishop, Christopher M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_993496
245 1 0 _aDeep Learning
_h[electronic resource] :
_bFoundations and Concepts /
_cby Christopher M. Bishop, Hugh Bishop.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXX, 649 p. 600 illus., 400 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aArtificial intelligence
_xData processing.
_921787
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aData Science.
_934092
700 1 _aBishop, Hugh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_993499
710 2 _aSpringerLink (Online service)
_993503
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031454677
776 0 8 _iPrinted edition:
_z9783031454691
856 4 0 _uhttps://doi.org/10.1007/978-3-031-45468-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c86935
_d86935