000			04188cam a2200565 i 4500
001			991051418079706532
003			OSt
005			20240104125300.0
008			181221s2019 maua b 001 0 eng
010			_a 2018059550
020			_a9780262537551 _qpaperback _qalkaline paper
020			_a0262537559 _qpaperback _qalkaline paper
024	8		_a40029360834
035			_a(CUY)UCB-b251116852-01ucs_ber
035			_a(OCoLC)1081370294
035			_a(OCoLC)on1081370294
035			_a(EXLNZ-01UCS_NETWORK)9913429274306531
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dBDX _dYDX _dYUS _dPSC _dOCLCQ _dKSU _dLMR _dIL4J6 _dOCLCO _dIVU
042			_apcc
049			_aMAIN
050	0	0	_aQ325.5 _b.K454 2019
082	0	0	_a006.3/1 _223
100	1		_aKelleher, John D., _d1974- _eauthor. _0http://id.loc.gov/authorities/names/n2014074189
245	1	0	_aDeep learning / _cJohn D. Kelleher.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2019]
300			_ax, 280 pages : _billustrations ; _c18 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aThe MIT press essential knowledge series
504			_aIncludes bibliographical references and index.
505	0	0	_tIntroduction to deep learning -- _tConceptual foundations -- _tNeural networks: the building blocks of deep learning -- _tA brief history of deep learning -- _tConvolutional and recurrent networks -- _tLearning functions -- _tThe future of deep learning.
520			_a"Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing and learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"-- _cProvided by publisher.
650		0	_aMachine learning. _0http://id.loc.gov/authorities/subjects/sh85079324
650		0	_aArtificial intelligence. _0http://id.loc.gov/authorities/subjects/sh85008180
650		6	_aApprentissage automatique.
650		6	_aIntelligence artificielle.
650		7	_aartificial intelligence. _2aat
650		7	_aArtificial intelligence. _2fast _0(OCoLC)fst00817247
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795
650		7	_aMachine learning. _2nli
650		7	_aArtificial intelligence. _2nli
830		0	_aMIT Press essential knowledge series. _0http://id.loc.gov/authorities/names/no2011178343
908			_aWorldCat Daily Updates 2023-10-20
942			_2udc _cBK _h82-3/KEL
999			_c22258 _d22258