000			02040nam a2200349 i 4500
999			_c39031 _d39031
001			32607
003			BD-DhAAL
005			20211114152523.0
008			180115t20162016maua b 001 0 eng
010			_a 2016022992
020			_a9780262035613 (hardcover : alk. paper)
020			_a0262035618 (hardcover : alk. paper)
040			_aDLC _beng _cDLC _erda _dDLC _dBD-DhAAL
042			_apcc
050	0	0	_aQ325.5 _b.G66 2016
082	0	0	_a006.31 _223
100	1		_aGoodfellow, Ian _924830
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
260			_aCambridge, Massachusetts : _bThe MIT Press, _cc2016
300			_axxii, 775 pages : _billustrations (some color) ; _c24 cm.
490	0		_aAdaptive computation and machine learning
504			_aIncludes bibliographical references (pages [711]-766) and index.
505	0		_aApplied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
526			_aAAL
650		0	_aMachine learning, _924831
650		0	_aComputer science. _942418
700	1		_aBengio, Yoshua _924832
700	1		_aCourville, Aaron _924833
852			_aAyesha Abed Library _cGeneral Stacks
942			_2ddc _cBK