000			a
999			_c33992 _d33992
008			250601b xxu||||| |||| 00| 0 eng d
020			_a9783031454677
082			_a006.31 _bBIS
100			_aBishop, Christopher M.
245			_aDeep learning : foundations and concepts
260			_bSpringer, _c2024 _aCham :
300			_axx, 649 p. ; _bill., (chiefly col.)ports., _c26 cm
365			_b79.99 _c € _d100.40
504			_aIncludes bibliographical references 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.
650			_aConditional distribution
650			_aGaussian distribution
650			_aKullback-Leibler divergence
650			_aLikelihood function
650			_aPosterior probabilities
650			_aStochastic gradient descent
700			_aBishop, Hugh
942			_2ddc _cBK