000			01759nam\a2200301\a\4500
001			57809
005			20260519155225.0
008			260519t20162016maua b 001 0 eng
020			_a9780262035613 _qhardcover : alk. paper
020			_a0262035618 _qhardcover : alk. paper
040			_aDLC _beng _cDLC _erda _dBD-DhIUB
082	0	0	_a006.31 _223 _bG6461d
100	1		_aGoodfellow, Ian, _eauthor.
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
250			_aEngland:
260			_aEngland: _bThe MIT press: _c2018
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			_aLIB _bps _lREF
541			_aOmni concept
650		0	_aMachine learning,
700	1		_aBengio, Yoshua, _eauthor.
700	1		_aCourville, Aaron, _eauthor.
942			_2ddc _cBK
999			_c57809 _d57983