Deep learning for numerical applications with SAS

Bequet, Henry, author.

2018, eBook , 1 online resource (1 volume) :

4313770

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Summary/Review: Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS Dive into deep learning! Mach more...

Summary/Review: 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.

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Full Bibliographic Record
Publication information:	Cary, NC : SAS Institute, [2018]
Copyright:	©2018
ISBN:	1635266777
ISBN:	9781635266771
Language:	English
Record ID:	4313770
Format:	eBook
Physical description:	1 online resource (1 volume) : illustrations
Date acquired:	October 13, 2022
More creator details:	Henry Bequet.
Contents note:	Chapter 4: Many-Task ComputingA Taxonomy for Parallel Programs; Tasks Are the New Threads; What Is a Task?; Inputs and Outputs; Immutable Inputs; What Is a Job Flow?; Examples of Job Flows; Mutable Inputs; Task Revisited; Partitioning; Federated Areas; Persistent Area; Caveats and Pitfalls; Not Declaring Your Inputs; Not Treating Your Immutable Inputs as Immutable; Not Declaring Your Outputs; Performance of Grid Scheduling; Data-Object Pooling; Portable Learning; Conclusion; Chapter 5: Monte Carlo Simulations; Monte Carlo or Las Vegas?; Random Walk; Multi-threaded Random Walk; SAS Studio
Contents note:	Intro; Contents; Preface; About This Book; What Does This Book Cover?; Is This Book for You?; What Are the Prerequisites for This Book?; What Should You Know about the Examples?; Software Used to Develop the Book's Content; Example Code and Data; We Want to Hear from You; About The Author; Acknowledgments; Chapter 1: Introduction; Deep Learning; Is Deep Learning for You?; It's All about Performance; Flynn's Taxonomy; Life after Flynn; Organization of This Book; Chapter 2: Deep Learning; Deep Learning; Connectionism; The Perceptron; The First AI Winter; The Experts to the Rescue
Contents note:	Live ETLA Parallel Program; A Parallel Program with Partitions; Many Cores; Conclusion; Chapter 6: GPU; History of GPUs; The Golden Age of the Multicore; The Golden Age of the Graphics Card; The Golden Age of the GPU; The CUDA Programming Model; Hello; The CUDA Toolkit; Buffon Revisited; Generating Random Walk Data with CUDA; Putting It All Together; Conclusion; Chapter 7: Monte Carlo Simulations with Deep Learning; Generating Data; Training Data; Testing Data; Training the Network; Inference Using the Network; Performance Summary; Other Examples; Pricing of American Options
Contents note:	Pricing of Variable Annuities ContractsConclusion; Chapter 8: Deep Learning for Numerical Applications in the Enterprise; Enterprise Applications; A Task; Data; Task Implementation; A Simple Flow; A Training Flow Task; An Inference Flow; Documentation; Heterogeneous Architectures; Collaboration with Federated Areas; Deploying DL with Federated Areas; Conclusions; Chapter 9: Conclusions; Data-Driven Programming; The Quest for Speed; From Tasks to GPUs; Training and Inference; FPGA; Hybrid Architectures; Appendix A: Development Environment Setup; LINUX; Windows; References; Index
Contents note:	The Second AI WinterThe Deeps; The Third AI Winter; Some Supervision Required; A Few Words about CAS; Deployment Models; CAS Sessions; Caslibs; Workers; Action Sets and Actions; Cleanup; All about the Data; The Men Body Mass Index Data Set; The IRIS Data Set; Logistic Regression; Preamble; Create the ANN; Training; Inference; Conclusion; Chapter 3: Regressions; A Brief History of Regressions; All about the Data (Reprise); The CARS Data Set; A Simple Regression; The Universal Approximation Theorem; Universal Approximation Framework; Approximation of a Continuous Function; Conclusions
Contributor:	O'Reilly for Higher Education (Firm), distributor.
Electronic access:	Access online
Access URL:	https://ezproxy.torontopubliclibrary.ca/login?url=https://learning.oreilly.com/library/view/~/9781635266771/?ar

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Deep learning for numerical applications with SAS

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