But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you'll also learn how to make it sing.With this book, you'll explore:How Spark SQL's new interfaces improve performance over SQL's RDD data structureThe choice between data joins in Core Spark and Spark SQLTechniques for getting the most out of standard RDD transformationsHow to work around performance issues in Spark's key/value pair paradigmWriting high-performance Spark code without Scala or the JVMHow to test for functionality and performance when applying suggested improvementsUsing Spark MLlib and Spark ML machine learning librariesSpark's Streaming components and external community packages

    Based on the ubiquitous Java, it provides an alternative to daunting languages and expensive proprietary software.This book gives graphic designers, artists and illustrators of all stripes a jump start to working with processing by providing detailed information on the basic principles of programming with the language, followed by careful, step-by-step explanations of select advanced techniques.The author teaches computer graphics at NYU's Tisch School of the Arts, and his book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media.Previously announced as Pixels, Patterns, and Processing

    In this in-depth report, data scientist DJ Patil explains the skills, perspectives, tools and processes that position data science teams for success.Topics include: What it means to be "data driven." The unique roles of data scientists. The four essential qualities of data scientists. Patil's first-hand experience building the LinkedIn data science team.

    They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Consequently, organizations that understand these tools and know how to use them are benefiting at the expense of their rivals.Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies.This revised and fully updated edition contains several new sections and chapters, covering a broader set of topics than before, but retains the no-nonsense style of the original.Steven Finlay is a data scientist and author with more than 20 years’ experience of developing practical, business focused, analytical solutions. He holds a PhD in management science and is an honorary research fellow at Lancaster University in the UK.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    But what was once fantasy is now reality. With a growing glut of good used boats on the market, its possible to sail around the world in a boat that costs less than a car. In this fascinating book, well-known boating author John Vigor turns the spotlight on 20 seaworthy sailboats that are at home on the ocean. These are old fiberglass boats, mostly of traditional design and strong construction. All are small their sizes range from 20 feet to 32 feet overall but all have crossed oceans. Many have circumnavigated the world. And all are inexpensive. There are many hundreds of small cruising boats sailing the seven seas at this moment. They explore everywhere, from the ice-bound shores of Antarctica to the high latitudes of the Northern Hemisphere. Most, however, ply the tradewind routes where flying fish play. The water is warm, and coconut trees line calm lagoons bounded by beaches of pure white sand. But choosing the right boat to cross an ocean or go around the world can be a confusing and exasperating experience, particularly if your budget is tight. Its well-nigh impossible to find objective comparisons. Vigor sets out to remedy that in this book. He compares the designs and handling characteristics of 20 different boats whose prices on the secondhand market start at about $3,000. Interviews with experienced owners (featuring valuable tips about handling each boat in heavy weather) are interspersed with line drawings of hulls, sailplans, and accommodations. Vigor has unearthed the known weaknesses of each boat and explains how to deal with them. He rates their comparative seaworthiness, their speed, and the number of people they can carry in comfort. If you have ever dreamed the dream, this is the book that will turn it into reality.

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    Nowadays success in every single competitive examinations lime bank clerical,bank PO,LIC,GIC,MBA Assistant grade,excise & income tax,IAS,IFS,AAO,Railway hotel management and others depend much on the candiate's performance in the reasoning paper.so much comprehensive and intelligent approach to it is the need of the day.This book serves the purpose

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    Monmonier shows that, despite their immense value, maps lie. In fact, they must.The second edition is updated with the addition of two new chapters, 10 color plates, and a new foreword by renowned geographer H. J. de Blij. One new chapter examines the role of national interest and cultural values in national mapping organizations, including the United States Geological Survey, while the other explores the new breed of multimedia, computer-based maps.To show how maps distort, Monmonier introduces basic principles of mapmaking, gives entertaining examples of the misuse of maps in situations from zoning disputes to census reports, and covers all the typical kinds of distortions from deliberate oversimplifications to the misleading use of color."Professor Monmonier himself knows how to gain our attention; it is not in fact the lies in maps but their truth, if always approximate and incomplete, that he wants us to admire and use, even to draw for ourselves on the facile screen. His is an artful and funny book, which like any good map, packs plenty in little space."—Scientific American"A useful guide to a subject most people probably take too much for granted. It shows how map makers translate abstract data into eye-catching cartograms, as they are called. It combats cartographic illiteracy. It fights cartophobia. It may even teach you to find your way. For that alone, it seems worthwhile."—Christopher Lehmann-Haupt, The New York Times". . . witty examination of how and why maps lie. [The book] conveys an important message about how statistics of any kind can be manipulated. But it also communicates much of the challenge, aesthetic appeal, and sheer fun of maps. Even those who hated geography in grammar school might well find a new enthusiasm for the subject after reading Monmonier's lively and surprising book."—Wilson Library Bulletin"A reading of this book will leave you much better defended against cheap atlases, shoddy journalism, unscrupulous advertisers, predatory special-interest groups, and others who may use or abuse maps at your expense."—John Van Pelt, Christian Science Monitor"Monmonier meets his goal admirably. . . . [His] book should be put on every map user's 'must read' list. It is informative and readable . . . a big step forward in helping us to understand how maps can mislead their readers."—Jeffrey S. Murray, Canadian Geographic

    He includes information on Java Platform Standard Edition 6 (Java SE 6) and offers complete coverage of the Java language, its syntax, keywords, and fundamental programming principles.