000 02158aam a2200217 4500
008 241011b2024 |||||||| |||| 00| 0 eng d
020 _a9783031454677
082 _a006.31
_bB4D3
100 _aBishop, Christopher M.
_9385679
245 _aDeep learning: foundations and concepts
260 _bSpringer
_c2024
_aCham
300 _axx, 649 p.
_bincludes appendix, bibliography and 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. https://link.springer.com/book/10.1007/978-3-031-45468-4
650 _amachine learning
_957333
650 _aDeep learning
_9427610
650 _aNeural networks
_9138382
650 _aDecision theory
_9427611
650 _aDirected graphical models
_9427612
650 _aConvolutional networks
_9427613
942 _2ddc
_cBK
999 _c222677
_d222677