000			04384cam a2200529 i 4500
001			u15147
003			SA-PMU
005			20210418123321.0
008			160613t20162016maua b 001 0 eng
010			_a 2016022992
040			_aDLC _beng _erda _cDLC _dYDXCP _dBTCTA _dBDX _dOCLCF _dUUM _dMYG _dNRC _dWW9 _dDGU _dIUL _dEUM _dHEBIS _dOBE _dVP@ _dFDA _dTMA _dLIP _dOCLCO _dCTU _dCOH _dUKWOH _dOSU _dUCW _dVT2 _dU3W _dI8M _dUV0 _dCSA _dNJR _dGZN _dTEU _dAUD _dJ3G _dUWO _dBUF _dVTU _dJG0 _dLEATE _dAUW _dBGAUB _dCUY _dOKX _dYDX _dGILDS _dOCLCQ _dGZM
019			_a951226949 _a964650355 _a1009034043
020			_a9780262035613 _q(hardcover ; _qalkaline paper)
020			_a0262035618 _q(hardcover ; _qalkaline paper)
035			_a(OCoLC)955778308 _z(OCoLC)951226949 _z(OCoLC)964650355 _z(OCoLC)1009034043
042			_apcc
050	0	0	_aQ325.5 _b.G66 2016
072		7	_aCOM _2eflch
072		7	_aCOM _2ukslc
082	0	0	_a006.3/1 _223
084			_a006.31 _bGOO _2z
084			_a006.31 _bGOO
100	1		_aGoodfellow, Ian, _eauthor.
245	1	0	_aDeep learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2016]
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	1		_aAdaptive computation and machine learning
504			_aIncludes bibliographical references (pages 711-766) and index.
505	0		_aIntroduction -- APPLIED 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.
520			_a"Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors"--Page 4 of cover.
650		0	_aMachine learning.
650		7	_aComputers and IT. _2eflch
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795
650		7	_aMaschinelles Lernen _2gnd
650		7	_aComputers and IT. _2ukslc
650		4	_aMachine learning.
700	1		_aBengio, Yoshua, _eauthor.
700	1		_aCourville, Aaron, _eauthor.
830		0	_aAdaptive computation and machine learning.
856	4	2	_uhttp://www.deeplearningbook.org/
596			_a1
942			_cBOOK
999			_c2541 _d2541