Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn Get hands-on with the latest version of Spark ML Create your first Spark program with Scala and Python Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 Access public machine learning datasets and use Spark to load, process, clean, and transform data Use Spark's machine learning library to implement programs by utilizing well-known machine learning models Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models Write Spark functions to evaluate the performance of your machine learning models In Detail This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approach This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.
The Hundred Days that saw the British response to General Galtiere of Argentinas invasion of the Falklands are for many British people the most remarkable of their lives. It describes the dark days of early April, the feverish response and forming of the Task Force, the anxieties and uncertainties, the naval and air battles that preceded the landings by 3 Commando Brigade and 5th Infantry Brigade. The extraordinary battles such as Goose Green, Mount Tumbledown, Wireless Ridge etc are narrated fully but succinctly. This is a very balanced overview of a never-to-be-repeated but triumphant chapter in British military history.
In this best-selling introductory textbook, Janet Holmes and Nick Wilson examine the role of language in a variety of social contexts, considering both how language works and how it can be used to signal and interpret various aspects of social identity. Divided into three sections, this book explains basic sociolinguistic concepts in the light of classic approaches as well as introducing more recent research. This fifth edition has been revised and updated throughout using key concepts and examples to guide the reader through this fascinating area, including: a new chapter on identity that reflects the latest research; a brand new companion website which is fully cross-referenced within this book, and which includes and video and audio materials, interactive activities and links to useful websites; updated and revised examples and exercises which include new material from Tanzania, Wales, Paraguay and Timor-Leste; fully updated further reading and references sections. An Introduction to Sociolinguistics is the essential introductory text for all students of sociolinguistics and a splendid point of reference for students of English language studies, linguistics and applied linguistics.
Nine Battles to Stanley is a soldiers account of the ground fighting on South Georgia and the Falklands.??What makes this book unique is the fascinating and objective way the author describes the experiences, view points and comparative qualities of both sides to the conflict. Fresh light is shed on the whole campaign even the best known battles at Goose Green (where Col. H. Jones won his VC) and the night attack on Mount Tumbledown.
Passionately satirical and sharply observant, he is one of the most interesting of our playwrights" (Sunday Telegraph) The Dead Monkey - "Darke has something both hilarious and horrific to say about the decay of a marriage and he compels the attention while doing so." (Sunday Telegraph) The King of Prussia - "A meaty play...seethes with life, wit and ideas. Darke give shape to a Cornish identity that feels vital and real and has nothing to do with clay pipes and clotted cream. Like Cornwall's coves, it has many unexpected depths...It also raises questions about the points where justice, conscience and the law part company" (Financial Times) The Body - "The best moments in Nick Darke's play are extremely good - and not all good in the same way...the most obvious debt is to Brecht. There are other reminiscences of Auden and Isherwood's The Dog Beneath the Skin, of Tom Stoppard's After Magritte and of T.S Eliot's verse plays." Tin Tang Mine - "A lament for an industry and a way of life; but this should not suggest anything sentimental. The writing is rugged and muscular; lyrical but not ornate; vigorous but not folksy...oddball, quirkily parochial and as authentic as a slice of rough bread." (The Times)
Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book Get to the grips with the latest version of Apache Spark Utilize Spark's machine learning library to implement predictive analytics Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn Get hands-on with the latest version of Spark ML Create your first Spark program with Scala and Python Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 Access public machine learning datasets and use Spark to load, process, clean, and transform data Use Spark's machine learning library to implement programs by utilizing well-known machine learning models Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models Write Spark functions to evaluate the performance of your machine learning models In Detail This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approach This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.
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