How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables

    The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs.The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action.Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework.This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    In my view, there isn't higher praise than that."--Brian Keene, author of The Rising Meet Delphine, Simone, and Zarina. Together they are the Sisters of the Endless Night. For centuries the coven has traveled the width and breadth of the country, unable to call any one place home for very long. The black magic that has sustained their youth and beauty over the centuries requires frequent ritual human sacrifice. Thus they must move often to avoid detection by those who would oppose them. Now the coven has come to idyllic Wagner Avenue, the very heart of American suburbia. It is here where the alluring witches will face their gravest challenges yet. Dark forces are in motion, the cauldron is boiling, the pentagram is bleeding, and trouble is brewing. The witching hour is here. "Bryan Smith is one of the most reliable and consistently entertaining writers working in the horror and suspense genres."--Ray Garton, author of Live Girls

    But the path that Jewish immigrants took to enter British-controlled Palestine was a difficult one, fraught with danger and political intrigue. The boat was intercepted by British forces and the refugees were placed in concentration camps.Uris’s blockbuster novel traces the lives of the men and women who brave British naval blockades to help Israel come into being, from Ari Ben Canaan, who works tirelessly to smuggle in settlers, to Kitty Fremont, an American nurse drawn into a vast, tragic history. Weaving together fact and fiction, history and dramatic storylines, Exodus stands today as one of the most influential narratives of the founding of the State of Israel.In QB VII, for Abe Cady, settlement is not an option when the facts of the Holocaust are on trial. A journalist and screenwriter, Cady produced the definitive account of the Holocaust just after World War II. But Polish doctor Adam Kelno, who was pressed into service in a notorious concentration camp, sues Cady for his book’s claim that the doctor conducted terrible experiments on camp inmates. The libel trial that follows tears open old wounds, disrupts lives, and becomes a battle for justice on behalf of tens of thousands of lost and damaged souls.QB VII is a gripping drama, largely based on author Uris’s own protracted libel defense against a former concentration camp surgeon named in his novel Exodus. It was made into the first miniseries in television history.

     Arthur, Duke of Wellington, and Emperor Napoleon Bonaparte were adversaries on an epic scale. Across Europe and beyond, the armies of Great Britain and France clashed, from the Iberian Peninsula to India, from Austerlitz to the final confrontation at Waterloo. What drove the two clever, ambitious, determined men who masterminded these military campaigns? How did the underdog from Corsica develop the strategic military skills and the political cunning that gave him power over swathes of Europe? And how did Wellington, born to be a leader, hone his talents and drive an army to victory after victory?From an outstanding historian and novelist come four epic novels, now available in one volume for the first time, which tell the full story of both these men, from their very early days till the momentous battle at Waterloo which decided the future of Europe.INCLUDES MAPS

    It shattered everyone, leaving him with a burning need to right the wrongs they suffered. He will stop at nothing to recoup what they are owed. It’s not theft, it’s payback.Until his solo crusade falls foul of the very people he seeks to rob - the 1 percenters, the people who own the bulk of the world’s wealth. Soon he is putting together a crew to carry out one last robbery, to undertake one last job. Success will restore his family’s fortunes, but failure will destroy them forever.Packed with relentless action and the sort of riveting authenticity only Andy McNab can provide, Whatever It Takes tells the story of one man’s extraordinary pursuit of justice against devastating odds, a story as hard and real and controversial as any of today’s headlines, which will show the world as it really is.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.New features in the second edition include:an interactive CD with PowerPoint presentations and problems for each chapter including an overview of the problem's solution; new chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and one on nonparametric statistics; more graphs and more precise descriptions of each statistic; and a discussion of confidence intervals.This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.Materials formerly available with this book on CD-ROM are now available for download from our website www.psypress.com. Go to the book's page and look for the 'Download' link in the right-hand column.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    His summer job is to test a new virtual reality game using the Vortex Capsule, a machine capable of fully immersing you into virtual reality. His first session is life changing, and he quickly becomes engrossed in a new fantasy world. Things are going great until a rogue hacking group attacks the servers that host the game, trapping all the players inside. Things get even worse when a dark force gains power and has sinister plans for Endless Fantasy Online. Can Luke and his new friends survive the dark forces that are on the rise? Will he, and the other players, be able to cope with the fact that they can't return to Earth? Will Vortex Industries find the hacking group responsible and save the players? Find answers to all of this and more in Endless Fantasy Online: The Phoenix Kingdom!

    It covered jazz, of course, but it also included Davis’s ruminations on race, politics and culture. Fascinated, Hef sent the writer—future Pulitzer-Prize-winning author Alex Haley, an unknown at the time—back to glean even more opinion and insight from Davis. The resulting exchange, published in the September 1962 issue, became the first official Playboy Interview and kicked off a remarkable run of public inquisition that continues today—and that has featured just about every cultural titan of the last half century.To celebrate the Interview’s 50th anniversary, the editors of Playboy have culled 50 of its most (in)famous Interviews and will publish them over the course of 50 weekdays (from September 4, 2012 to November 12, 2012) via Amazon’s Kindle Direct platform. Here is that first Interview with Miles Davis.

    With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.Learn how to apply the tidy text format to NLPUse sentiment analysis to mine the emotional content of textIdentify a document's most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R's tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages