Deep learning : a practitioner's approach Josh Patterson and Adam Gibson.

By:

Patterson, Josh

Contributor(s):

Gibson, Adam

Material type: Text

TextPublisher: Sebastopol, CA : O'Reilly, 2017Edition: First editionDescription: 528 pISBN:

9781491914250

Subject(s):

DDC classification:

006.31 PAT 23

Summary: How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.-- Source other than Library of Congress.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Reference	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Not for loan		46225
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2024-10-14	46226
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2024-10-28	46227
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Available		46228
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2024-08-05	46229
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Available		46230
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2025-03-03	46231
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Available		46232
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2025-01-23	46233
Books	NMIT General Stacks	CSE	006.31 PAT (Browse shelf(Opens below))	Checked out	2025-01-23	46234

How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.-- Source other than Library of Congress.

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