000			02129cam a2200397 i 4500
001			19134018
003			VITAP
005			20250225182326.0
008			160613t20162016maua b 001 0 eng
010			_a 2016022992
020			_a9780262035613 (hardcover : alk. paper)
020			_a0262035618 (hardcover : alk. paper)
040			_aDLC _beng _cVITAP _erda _dDLC
042			_apcc
050	0	0	_aQ325.5 _b.G66 2016
082	0	0	_a006.31 GOO _223rd
100	1		_aGoodfellow, Ian, _eauthor.
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
250			_a1st ed.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2017]
264		4	_c©2016
300			_axxii, 775 pages : _billustrations (some color) ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
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.
650		0	_aMachine learning,
700	1		_aBengio, Yoshua, _eauthor.
700	1		_aCourville, Aaron, _eauthor.
856			_uhttps://mitpress.mit.edu/9780262035613/deep-learning/
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _e23rd _h006.31 _mGOO
999			_c46205 _d46205