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

By:

Howard, Jeremy

Contributor(s):

Sylvain Gugger

Language: English Publication details: c2020 Sebastopol, California O'Reilly MediaEdition: 1st edDescription: xxii, 594 p. ill. 23 cmISBN:

9789385889134

Subject(s):

Artificial intelligence; Machine learning; Python (Computer program language)

DDC classification:

006.312 HOW

Summary: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

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Holdings
Item type	Current library	Call number	Copy number	Status	Barcode
Reference	Anna Centenary Library 3RD FLOOR, A WING	006.312 HOW (Browse shelf(Opens below))		Not for loan	692064
Reference	Anna Centenary Library 3RD FLOOR, A WING	006.312 HOW;1 (Browse shelf(Opens below))	1	Not for loan	692065

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Includes index

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

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

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