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Deep learning for coders with fastai and PyTorch : = AI applications without a PhD /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning for coders with fastai and PyTorch :/ Jeremy Howard and Sylvain Gugger ; [foreword by Soumith Chintala].
Reminder of title:	AI applications without a PhD /
Author:	Howard, Jeremy.
other author:	Gugger, Sylvain.
Published:	Beijing ;O'Reilly Media, Inc., : c2020.,
Description:	xxiv, 594 p. :ill. (chiefly col.) ; : 24 cm.;
Notes:	"Powered by jupyter"--Cover.
Subject:	Data mining. -
ISBN:	9781492045526 (pbk.) :

Deep learning for coders with fastai and PyTorch : = AI applications without a PhD /
Howard, Jeremy.

Deep learning for coders with fastai and PyTorch :AI applications without a PhD /Jeremy Howard and Sylvain Gugger ; [foreword by Soumith Chintala]. - 1st ed. - Beijing ;O'Reilly Media, Inc.,c2020. - xxiv, 594 p. :ill. (chiefly col.) ;24 cm.

"Powered by jupyter"--Cover.

Part 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.

Deep 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.

ISBN: 9781492045526 (pbk.) :NT2173

Nat. Bib. No.: GBC0C4821bnbSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343 / H69 2020

Dewey Class. No.: 006.312

Deep learning for coders with fastai and PyTorch : = AI applications without a PhD /
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245 1 0 $a Deep learning for coders with fastai and PyTorch : $b AI applications without a PhD / $c Jeremy Howard and Sylvain Gugger ; [foreword by Soumith Chintala].
250 $a 1st ed.
260 # $a Beijing ; $a Sebastopol, Calif. : $b O'Reilly Media, Inc., $c c2020.
300 $a xxiv, 594 p. : $b ill. (chiefly col.) ; $c 24 cm.
500 $a "Powered by jupyter"--Cover.
500 $a Includes index.
505 0 # $a Part 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 # $a Deep 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 $a Data mining. $3 528622
650 # 0 $a Natural language processing (Computer science) $3 641811
650 # 0 $a Machine learning. $3 561253
650 # 0 $a Python (Computer program language) $3 566246
650 # 0 $a Artificial intelligence. $3 559380
700 1 # $a Gugger, Sylvain. $3 1386606
700 1 # $a Chintala, Soumith. $3 1386607