000			02777cam a2200265 i 4500
001			991025466924707026
003			UkOxU
005			20240527164132.0
008			240112s2024 sz ad b 001 0 eng d
020			_a9783031454677
035			_a(OCoLC)1417156995 _z(OCoLC)1395946546
035			_a(OCoLC)on1417156995
040			_aAKC _erda _beng _cAKC _dOCLCO _dYDX _dBDX _dUkOxU
050		4	_aQ325.73 _b.B574 2023
100	1		_aBishop, Christopher M., _eauthor.
245	1	0	_aDeep learning _b: foundations and concepts _c/ Christopher M. Bishop, Hugh Bishop.
264		1	_aCham : _bSpringer, _c[2024]
300			_axx, 649 pages : _billustrations, charts ; _c26 cm.
504			_aIncludes bibliographic references (pages 625-640) and index.
505	0		_aThe 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.
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.
650		0	_aDeep learning (Machine learning)
700	1		_aBishop, Hugh, _eauthor.
942			_2lcc
999			_c4131 _d4131