Deep Learning for Numerical Applications with SAS

Bequet, Henry

ISBN 10: 1635266807 / ISBN 13: 9781635266801

Published by SAS Institute, 2018

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Bibliographic Details

• Title: Deep Learning for Numerical Applications with SAS
• Publisher: SAS Institute
• Publication Date: 2018
• Language: English
• ISBN 10: 1635266807
• ISBN 13: 9781635266801
• Binding: paperback
• Condition: Very Good

About this title

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 SASŪ 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 SASŪ 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

This book is part of the SAS Press program.

About the Author

Henry Bequet is Director of High-Performance Computing and Machine Learning in the Financial Risk division of SAS. In that capacity, he leads the development of a high-performance solution that can run SAS code on thousands of CPU and GPU cores for advanced models that use techniques like Black-Scholes, Binomial Evaluation, and Monte-Carlo simulations. Henry has more than 35 years of industry experience and 15 years of high-performance analytics practice. He has published two books and several papers on server development and machine learning.