000			03156cam a2200493 i 4500
001			22962735
005			20240310114545.0
008			230207t20202020caua e 001 0 eng d
010			_a 2022278837
020			_a9781492045526 _q(paperback)
035			_a(OCoLC)on1184463764
035			_a(Sirsi) i9781492045526
040			_aSRB _beng _cSRB _erda _dOQX _dGO3 _dOCLCO _dOCLCF _dCLE _dUKMGB _dCDX _dUAP _dOCLCO _dOCL _dOCLCO _dOCLCQ _dDLC
042			_alccopycat
050	0	0	_aQA76.9.D343 _bH69 2020
082	0	4	_a006.312 _223
100	1		_aHoward, Jeremy _c(Scientist), _eauthor _93292
245	1	0	_aDeep learning for coders with fastai and PyTorch : _bAI applications without a PhD _c/ Jeremy 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			_astill image _bsti _2rdacontent
336			_atext _btxt _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.
596			_a1 3
598			_aNEWBOOKS
650		0	_aArtificial intelligence. _93262
650		0	_aData mining. _92728
650		0	_aMachine learning. _92745
650		0	_aNatural language processing (Computer science) _93293
650		0	_aPython (Computer program language) _91138
650		2	_aArtificial intelligence _93294
650		2	_aData mining _93295
650		2	_aNatural language processing (Computer science) _93296
700	1		_aChintala, Soumith, _ewriter of foreword _93297
700	1		_aGugger, Sylvain, _eauthor _93298
710	2		_aSafari, an O'Reilly Media Company _93299
999			_c1004 _d1004