000			01652cam a2200313 i 4500
003			CUTN
005			20181004112428.0
008			160613t20162016maua b 001 0 eng
020			_a9780262035613
020			_a0262035618
041			_aEnglish
082	0	0	_a006.31 _223 _bGOO
100	1		_aGoodfellow, Ian,
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
260			_aCambridge, MA : _bMIT Press, _c2017.
300			_axxii, 775 pages : _billustrations (some color) ; _c24 cm.
505	0		_tLinear algebra -- _tProbability and information theory -- _tNumerical computation -- _tMachine learning basics -- _tDeep feedforward networks -- _tRegularization for deep learning -- _tOptimization for training deep models -- _tConvolutional networks -- _tSequence modeling: recurrent and recursive nets -- _tPractical methodology -- _tApplications -- _tLinear factor models -- _tAutoencoders -- _tRepresentation learning -- _tStructured probabilistic models for deep learning -- _tMonte Carlo methods -- _tConfronting the partition function -- _tApproximate inference -- _tDeep generative models.
650		0	_aMachine learning,
700	1		_aBengio, Yoshua,
700	1		_aCourville, Aaron,
942			_2ddc _cBOOKS
100	1		_eauthor.
490	0		_aAdaptive computation and machine learning
504			_aIncludes bibliographical references (pages 711-766) and index.
700	1		_eauthor.
700	1		_eauthor.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
999			_c26007 _d26007