Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    In this one-of-a-kind account, you'll see how the developers responsible for these flaws patched the bugs—or failed to respond at all. As you follow Klein on his journey, you'll gain deep technical knowledge and insight into how hackers approach difficult problems and experience the true joys (and frustrations) of bug hunting.Along the way you'll learn how to:Use field-tested techniques to find bugs, like identifying and tracing user input data and reverse engineering Exploit vulnerabilities like NULL pointer dereferences, buffer overflows, and type conversion flaws Develop proof of concept code that verifies the security flaw Report bugs to vendors or third party brokersA Bug Hunter's Diary is packed with real-world examples of vulnerable code and the custom programs used to find and test bugs. Whether you're hunting bugs for fun, for profit, or to make the world a safer place, you'll learn valuable new skills by looking over the shoulder of a professional bug hunter in action.

    Feynman gave his famous course on computation at the California Institute of Technology, he asked Tony Hey to adapt his lecture notes into a book. Although led by Feynman, the course also featured, as occasional guest speakers, some of the most brilliant men in science at that time, including Marvin Minsky, Charles Bennett, and John Hopfield. Although the lectures are now thirteen years old, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science such as reversible logic gates and quantum computers.

    Elements of programming.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Topics are broken down into three major conceptual pieces: Virtualization, Concurrency, and Persistence. Includes all major components of modern systems including scheduling, virtual memory management, disk subsystems and I/O, file systems, and even a short introduction to distributed systems.

    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

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

     Master each chapter with free interactive exercises online. Live simulation lets you see your practice code run in your browser. 2,000 lines of color-keyed sample code break it all down into easy-to-learn chunks. Extra help through the rough spots so you're less likely to get stuck. Tested on non-coders—including the author's technophobe wife. Become fluent in all the JavaScript fundamentals, in half the time. Display alert messages to the user Gather information through prompts Manipulate variables Build statements Do math Use operators Concatenate text Run routines based on conditions Compare values Work with arrays Run automated routines Display custom elements on the webpage Generate random numbers Manipulate decimals Round numbers Create loops Use functions Find the current date and time Measure time intervals Create a timer Respond to the user's actions Swap images Control colors on the webpage Change any element on the webpage Improvise new HTML markup on the fly Use the webpage DOM structure Insert comments Situate scripts effectively Create and change objects Automate object creation Control the browser's actions Fill the browser window with custom content Check forms for invalid entries Deal with errors Make a more compelling website Increase user-friendliness Keep your user engaged

    It's Tuesday morning and on his drive into the office, Bill gets a call from the CEO. The company's new IT initiative, code named Phoenix Project, is critical to the future of Parts Unlimited, but the project is massively over budget and very late. The CEO wants Bill to report directly to him and fix the mess in ninety days or else Bill's entire department will be outsourced. With the help of a prospective board member and his mysterious philosophy of The Three Ways, Bill starts to see that IT work has more in common with manufacturing plant work than he ever imagined. With the clock ticking, Bill must organize work flow streamline interdepartmental communications, and effectively serve the other business functions at Parts Unlimited. In a fast-paced and entertaining style, three luminaries of the DevOps movement deliver a story that anyone who works in IT will recognize. Readers will not only learn how to improve their own IT organizations, they'll never view IT the same way again.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    And they did it without fanfare or recognition from their employer. Hiltzik's Dealers of Lightning provides a fascinating look at technohistory that sets the record straight. In Dealers of Lightning, Hiltzik describes the forces and faces behind the revolution that the Xerox PARC team single-handedly spawned. The Xerox PARC group was composed solely of top technical minds. The decision was made at Xerox headquarters to give the team complete freedom from deadlines and directives, in hopes of fostering a true creative environment. It worked — perhaps too well. The team responded with a steady output of amazing technology, including the first version of the Internet, the first personal computer, user-friendly word-processing programs, and pop-up menus. Xerox, far from ready for the explosion of innovation, failed to utilize the technology dreamed up by the group. Out of all the dazzling inventions born at Xerox PARC, only a handful were developed and marketed by Xerox. However, one of these inventions, the laser printer, proved successful enough to earn billions for the company, therefore justifying its investment in the research center. Most oftheteam's creations would go on to be developed and perfected by other companies, such as IBM, Apple, and Microsoft. Drawing from interviews with the engineers, executives, and scientists involved in the Xerox PARC, Dealers of Lightning chronicles an amazing era of egos, ideas, and inventions at the dawn of the computer age.

    Max 'Vision' Butler was a white-hat hacker and a celebrity throughout the programming world, even serving as a consultant to the FBI. But there was another side to Max. As the black-hat 'Iceman', he'd seen the fraudsters around him squabble, their ranks riddled with infiltrators, their methods inefficient, and in their dysfunction was the ultimate challenge: he would stage a coup and steal their ill-gotten gains from right under their noses.Through the story of Max Butler's remarkable rise, KINGPIN lays bare the workings of a silent crime wave affecting millions worldwide. It exposes vast online-fraud supermarkets stocked with credit card numbers, counterfeit cheques, hacked bank accounts and fake passports. Thanks to Kevin Poulsen's remarkable access to both cops and criminals, we step inside the quiet,desperate battle that law enforcement fights against these scammers. And learn that the boy next door may not be all he seems.