Deep Learning for Numerical Applications with SAS (Hardcover edition)

Bequet, Henry

Hardback (16 Aug 2019)

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Publisher's Synopsis

Foreword by Oliver Schabenberger, PhD
Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS

Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces-from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks.

Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs.

This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to

Use deep learning in SAS
Speed up their analytics using deep learning
Easily write highly parallel programs using the many task computing paradigms

ISBN:	9781642953565
Publisher:	SAS Institute
Imprint:	SAS Institute
Pub date:	16 Aug 2019
Language:	English
Number of pages:	234
Weight:	617g
Height:	235mm
Width:	191mm
Spine width:	14mm