000			04152cam a2200733 i 4500
001			22962735
003			JO-AjAnu
005			20240610092232.0
008			230207t20202020caua e 001 0 eng d
010			_a 2022278837
015			_aGBC0C4821 _2bnb
016	7		_a019897979 _2Uk
020			_a9781492045526 _q(paperback)
020			_a1492045527 _q(paperback)
035			_a(OCoLC)on1184463764
040			_aSRB _beng _cSRB _erda _dOQX _dGO3 _dOCLCO _dOCLCF _dCLE _dUKMGB _dCDX _dUAP _dOCLCO _dOCL _dOCLCO _dOCLCQ _dDLC _dJO-AjAnu
041			_aeng _heng
042			_alccopycat
050	0	0	_aQA76.9.D343 _bH69 2020
082	0	4	_a006.312 _223
100	1		_aHoward, Jeremy _c(Scientist), _eauthor.
245	1	0	_aDeep learning for coders with fastai and PyTorch : _bAI applications without a PhD / _cJeremy Howard and Sylvain Gugger ; [foreword by Soumith Chintala].
250			_aFirst edition.
264		1	_aSebastopol, California : _bO'Reilly Media, Inc., _c2020.
264		4	_c©2020
300			_axxiv, 594 pages : _billustrations (chiefly color) ; _c24 cm
336			_atext _btxt _2rdacontent
336			_astill image _bsti _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_a"Powered by jupyter"--Cover
500			_aIncludes index
505	0		_aPart 1. Deep Learning Journey. Your Deep Learning Journey -- From Model to Production -- Data Ethics -- Part 2. Understanding fastai's Applications. Under the Hood: Training a Digit Classifier -- Image Classification --Other Computer Vision Problems -- Training a State-of-the-Art Model -- Collaborative Filtering Deep Dive -- Tabular Modeling Deep Dive -- NLP Deep Dive: RNNs -- Data Munging with fastai's Mid-Level API -- Part 3. Foundations of Deep Learning. A Language Model from Scratch -- Convolutional Neural Networks -- ResNets -- Application Architectures Deep Dive -- The Training Process -- Part 4. Deep Learning from Scratch. A Neural Net from the Foundations -- CNN Interpretation with CAM -- A fastai Learner from Scratch -- Concluding Thoughts.
520			_aDeep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a wide range of tasks-including computer vision, natural language processing, tabular data, and generative networks. At the same time, you'll dig progressively into deep learning theory so that by the end of the book you'll have a complete understanding of the math behind the library's functions.
650		0	_aData mining.
650		0	_aNatural language processing (Computer science)
650		0	_aMachine learning.
650		0	_aPython (Computer program language)
650		0	_aArtificial intelligence.
650		2	_aData Mining
650		2	_aNatural Language Processing
650		2	_aArtificial Intelligence
650		6	_aExploration de données (Informatique)
650		6	_aTraitement automatique des langues naturelles.
650		6	_aApprentissage automatique.
650		6	_aPython (Langage de programmation)
650		6	_aIntelligence artificielle.
650		7	_aartificial intelligence. _2aat
650		7	_aNatural language processing (Computer science) _2fast _0(OCoLC)fst01034365
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aArtificial intelligence. _2fast _0(OCoLC)fst00817247
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795
650		7	_aNeural networks (Computer science) _2fast _0(OCoLC)fst01036260
650		7	_aPython (Computer program language) _2fast _0(OCoLC)fst01084736
655		2	_aHandbook
655		7	_aHandbooks and manuals. _2lcgft
655		7	_aGuides et manuels. _2rvmgf
700	1		_aGugger, Sylvain, _eauthor.
700	1		_aChintala, Soumith, _ewriter of foreword.
710	2		_aSafari, an O'Reilly Media Company.
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c33526 _d33526