000			01601 n a2200289 4500
001			u3669
003			SIRSI
005			20260302130319.0
008			220928s2016 us ad frf 100 engnd d
020			_a9780262035613
040			_aclrauoh _bspa _cclrauoh _erda _dclrauoh
082			_2006.31
100			_aGoodfellow, Ian. _eauthor.
245	1	0	_aDeep Learning / _cIan Goodfellow ; Yoshua Begio ; Aaron Courville.
260			_aCambridge, MA : _bMIT Press, _c2016
300			_axxii, 775 pages : _billustrations.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
440			_aAdaptive computation and machine learning.
504			_aIncludes bibliographical references and index.
505			_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			_aAprendizaje automático _xInteligencia artificial _2localuoh
700	1		_aBengio, Yoshua. _eauthor
700	1		_aCourville, Aaron. _eauthor
942			_2ddc _cBOOK
999			_c2251 _d2251