If the idea of a computer science book without computers upsets you, please close your eyes until you’ve finished reading the rest of this page.The truth is that computer science is not really about the computer. It is just a tool to help you see ideas more clearly. You can see the moon and stars without a telescope, smell the flowers without a fluoroscope, have fun without a funoscope, and be silly sans oscilloscope.You can also play with computer science without... you-know-what. Ideas are the real stuff of computer science. This book is about those ideas, and how to find them.

    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

    The book encourages a critical discussion of the different approaches and looks at some of the most important thinkers and their influence.

    The latest edition offers greatly expanded coverage of directed mutagenesis and protein engineering, therapeutic agents, and genetic engineering of plants. Updated chapters reflect recent developments in biotechnology and the societal issues related to it, such as cloning, gene therapy, and patenting and releasing genetically engineered organisms. Over 480 figures, including 200 that are new in this edition, illustrate all key concepts. "Milestones" summarize important research papers in the history of biotechnology and their effects on the field. As in previous editions, the authors clearly explain all concepts and techniques to provide maximum understanding of the subject, avoiding confusing scientific jargon and excessive detail wherever possible. Each chapter concludes with a summary, references, and review questions. Ideally suited as a text for third- and fourth-year undergraduates as well as graduate students, this book is also an excellent reference for health professionals, scientists, engineers, or attorneys interested in biotechnology.

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.

    In this cookbook, prolific hacker and author Simon Monk provides more than 200 practical recipes for running this tiny low-cost computer with Linux, programming it with Python, and hooking up sensors, motors, and other hardware—including Arduino.You’ll also learn basic principles to help you use new technologies with Raspberry Pi as its ecosystem develops. Python and other code examples from the book are available on GitHub. This cookbook is ideal for programmers and hobbyists familiar with the Pi through resources such as Getting Started with Raspberry Pi (O’Reilly).Set up and manage your Raspberry PiConnect the Pi to a networkWork with its Linux-based operating systemUse the Pi’s ready-made softwareProgram Raspberry Pi with PythonControl hardware through the GPIO connectorUse Raspberry Pi to run different types of motorsWork with switches, keypads, and other digital inputsHook up sensors for taking various measurementsAttach different displays, such as an LED matrixCreate dynamic projects with Raspberry Pi and Arduino Make sure to check out 10 of the over 60 video recipes for this book at: http://razzpisampler.oreilly.com/ You can purchase all recipes at:

    Servers give sysadmins a incredible visibility into the network—once they know how to unlock it. Most sysadmins don’t need to understand window scaling, or the differences between IPv4 and IPv6 echo requests, or other intricacies of the TCP/IP protocols. You need only enough to deploy your own applications and get easy support from the network team.This book teaches you:•How modern networks really work•The essentials of TCP/IP•The next-generation protocol, IPv6•The right tools to diagnose network problems, and how to use them•Troubleshooting everything from the physical wire to DNS•How to see the traffic you send and receive•Connectivity testing•How to communicate with your network team to quickly resolve problemsA systems administrator doesn’t need to know the innards of TCP/IP, but knowing enough to diagnose your own network issues transforms a good sysadmin into a great one.

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    D.S. Malik is ideal for a one-semester course focused on data structures. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). This student-friendly text features abundant Programming Examples and extensive use of visual diagrams to reinforce difficult topics. Students will find Dr. Malik's use of complete programming code and clear display of syntax, explanation, and example easy to read and conducive to learning.

    The data world was revolutionized a few years ago when Hadoop and other tools made it possible to getthe results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.

     They were preparing to compete in a poker tournament with million-dollar stakes, but those numbers meant nothing to them.  They were accustomed to risking billions.     At the card table that night was Peter Muller, an eccentric, whip-smart whiz kid who’d studied theoretical mathematics at Princeton and now managed a fabulously successful hedge fund called PDT…when he wasn’t playing his keyboard for morning commuters on the New York subway.  With him was Ken Griffin, who as an undergraduate trading convertible bonds out of his Harvard dorm room had outsmarted the Wall Street pros and made money in one of the worst bear markets of all time.  Now he was the tough-as-nails head of Citadel Investment Group, one of the most powerful money machines on earth. There too were Cliff Asness, the sharp-tongued, mercurial founder of the hedge fund AQR, a man as famous for his computer-smashing rages as for his brilliance, and Boaz Weinstein, chess life-master and king of the credit default swap, who while juggling $30 billion worth of positions for Deutsche Bank found time for frequent visits to Las Vegas with the famed MIT card-counting team.     On that night in 2006, these four men and their cohorts were the new kings of Wall Street.  Muller, Griffin, Asness, and Weinstein were among the best and brightest of a  new breed, the quants.  Over the prior twenty years, this species of math whiz --technocrats who make billions not with gut calls or fundamental analysis but with formulas and high-speed computers-- had usurped the testosterone-fueled, kill-or-be-killed risk-takers who’d long been the alpha males the world’s largest casino.  The quants believed that a dizzying, indecipherable-to-mere-mortals cocktail of differential calculus, quantum physics, and advanced geometry held the key to reaping riches from the financial markets.  And they helped create a digitized money-trading machine that could shift billions around the globe with the click of a mouse.     Few realized that night, though, that in creating this unprecedented machine, men like Muller, Griffin, Asness and Weinstein had sowed the seeds for history’s greatest financial disaster.     Drawing on unprecedented access to these four number-crunching titans, The Quants tells the inside story of what they thought and felt in the days and weeks when they helplessly watched much of their net worth vaporize – and wondered just how their mind-bending formulas and genius-level IQ’s had led them so wrong, so fast.  Had their years of success been dumb luck, fool’s gold, a good run that could come to an end on any given day?  What if The Truth they sought -- the secret of the markets -- wasn’t knowable? Worse, what if there wasn’t any Truth?   In The Quants, Scott Patterson tells the story not just of these men, but of Jim Simons, the reclusive founder of the most successful hedge fund in history; Aaron Brown, the quant who used his math skills to humiliate Wall Street’s old guard at their trademark game of Liar’s Poker, and years later found himself with a front-row seat to the rapid emergence of mortgage-backed securities; and gadflies and dissenters such as Paul Wilmott, Nassim Taleb, and Benoit Mandelbrot.     With the immediacy of today’s NASDAQ close and the timeless power of a Greek tragedy, The Quants is at once a masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.As “perhaps the best popular economics writer in the world” (New Statesman), Tim Harford is an expert at taking complicated ideas and untangling them for millions of readers. In The Data Detective, he uses new research in science and psychology to set out ten strategies for using statistics to erase our biases and replace them with new ideas that use virtues like patience, curiosity, and good sense to better understand ourselves and the world. As a result, The Data Detective is a big-idea book about statistics and human behavior that is fresh, unexpected, and insightful.

    This edition also features the developments in Operations Research, such as metaheuristics, simulation, and spreadsheet modeling.