000			02135cam a2200373 i 4500
001			19134018
003			BD-ChPU
005			20250114103401.0
008			160613t20162016maua b 001 0 eng
010			_a 2016022992
020			_a9780262035613 (hardcover : alk. paper)
020			_a0262035618 (hardcover : alk. paper)
040			_aDLC _beng _cDLC _erda _dDLC _dBD-ChPU
042			_apcc
050	0	0	_aQ325.5 _b.G66 2016
082	0	0	_a006.3/1 G651d 2017 _223
100	1		_aGoodfellow, Ian. _eauthor.
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio and Aaron Courville.
250			_aFirst edition.
260			_aMassachusetts : _bThe MIT Press, _c2017.
300			_axiv, 785 pages : _billustrations ; _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			_aComputer Science & Engineering
650		0	_aMachine learning. _93635
650		0	_aApplied math and machine learning basics _vProbability and information theory _vMachine learning basics _xLinear algebra.
650		0	_aDeep networks : modern practices. Deep feedforward networks
700	1		_aBengio, Yoshua. _eauthor.
700	1		_aCourville, Aaron. _eauthor.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c7659 _d7659