03002nam 2200457 450 991083107980332120210610080755.01-119-55672-41-119-55673-21-119-55674-0(CKB)4100000008965339(MiAaPQ)EBC5849505(PPN)272715654(CaSebORM)9781119556718(OCoLC)1117320705(EXLCZ)99410000000896533920190917d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine learning in the AWS Cloud add intelligence to applications with Amazon SageMaker and Amazon Rekognition /Abhishek Mishra1st editionIndianapolis, Indiana :Sybex,[2019]©20191 online resource (530 pages)1-119-55671-6 Includes bibliographical references and index.Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. •    Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building •    Discover common neural network frameworks with Amazon SageMaker •    Solve computer vision problems with Amazon Rekognition •    Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.Machine learningMachine learning.006.31Mishra Abhishek887481MiAaPQMiAaPQMiAaPQBOOK9910831079803321Machine learning in the AWS Cloud4029720UNINA02332oam 2200529I 450 991015456470332120230810001505.01-351-86872-10-89503-195-71-315-23266-910.4324/9781315232669 (CKB)3710000000966004(MiAaPQ)EBC4758343(OCoLC)973039328(BIP)63369869(BIP)48257359(EXLCZ)99371000000096600420180706e20171999 uy 0engurcnu||||||||rdacontentrdamediardacarrierAging public policy bonding the generations /Theodore H. Koff and Richard W. ParkSecond edition.London ;New York :Routledge,2017.1 online resource (448 pages)Society and Aging Series"First published 1999 by Baywood Publishing Company, Inc."--T.p. verso.0-89503-196-5 1-351-86873-X Includes bibliographical references and index.pt. 1. The policy process : responding to human needs through laws -- pt. 2. Aging policy development -- pt. 3. Major public policies on behalf of the elderly -- pt. 4. The advocacy process in the field of aging : policies for the puture."Aging Public Policy: Bonding the Generations" is presented in three parts. Part One describes the policy process as a response to human needs through the laws of our country. Part Two explores the national policy development on behalf of older persons. Part Three describes the major public policies on behalf of the elderly that include Social Security, Medicare, The Older Americans Act, institutional care, employment and retirement policies. The final chapter discusses the advocacy process in the field of aging.Society and aging series.Older peopleGovernment policyUnited StatesOlder peopleGovernment policy362.6/0973Koff Theodore H.932860Park Richard W932861MiAaPQMiAaPQMiAaPQBOOK9910154564703321Aging public policy2099656UNINA