000			02438nam a22004457a 4500
001			19134018
003			Ge_NSL
005			20231227114510.0
008			231227s20162016us ||||| |||| 00| 0 eng d
010			_a 2016022992
020			_a9780262035613 (hardcover : alk. paper)
020			_a0262035618 (hardcover : alk. paper)
040			_aDLC _beng _cDLC _erda _dDLC
041			_aeng
042			_apcc
050	0	0	_aQ325.5 _b.G66 2016
080			_a004
082	0	0	_a006.3/1 _223
100	1		_aGoodfellow, Ian _9285315
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio and Aaron Courville.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2016], ©2016.
300			_axxii, 775 p. : _bill. (some col.) ; _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, _95593
653			_aსაინფორმაციო ტექნოლოგიები
653			_aკომპიუტერული ტექნოლოგიები
653			_aმანქანური სწავლება
653			_aმანქანური სწავლების საფუძვლები
653			_aგამოყენებიტი მათემატიკა
700	1		_aBengio, Yoshua _9285316
700	1		_aCourville, Aaron _9285317
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2udc _cBK
999			_c726271 _d726269