It explains what algorithms do, how they are used, the risks associated with using them, and why governments should be concerned. Important areas arehighlighted, such as Stream Ciphers, block ciphers, public key algorithms, digital signatures, and applications such as e-commerce. This book highlights the explosive impact of cryptography on modern society, with, for example, the evolution of the internet and the introduction of more sophisticatedbanking methods.

    Yet many may be surprised to learn that the true story behind the making of the film is as captivating as the film itself. In The Making of Slap Shot, veteran sports writer Jonathon Jackson lets fans not only relive just how the film was made, but brings to light surprising facts (i.e., Al Pacino was the first choice for the role of Reggie Dunlop; almost every scene-even the absurd and unbelievable ones-depicts a real life event). With access to those involved in the making of the film, he brings to life some of the magic behind the creation of memorable scenes and characters, especially the Charleston Chiefs, one of the most popular fictional sports teams in history. Based on interviews with over 50 cast members, production staff, and anyone of note involved in the film's creation Destined to be a collectible and keepsake (along with the jerseys, bobbleheads, and other paraphernalia associated with the film), The Making of Slapshot is a must for fans eager to learn even more about their favorite film.

    Along with a clear introduction to asynchronous programming, you get an in-depth look at how the async feature works and why you might want to use it in your application.Written for experienced C# programmers—yet approachable for beginners—this book is packed with code examples that you can extend for your own projects.Write your own asynchronous code, and learn how async saves you from this messy choreDiscover new performance possibilities in ASP.NET web server codeExplore how async and WinRT work together in Windows 8 applicationsLearn the importance of the await keyword in async methodsUnderstand which .NET thread is running your code—and at what points in the programUse the Task-based Asynchronous Pattern (TAP) to write asynchronous APIs in .NETTake advantage of parallel computing in modern machinesMeasure async code performance by comparing it with alternatives

    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.

    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

    But if one per year is good, how about Seven Languages in Seven Weeks? In this book you'll get a hands-on tour of Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby. Whether or not your favorite language is on that list, you'll broaden your perspective of programming by examining these languages side-by-side. You'll learn something new from each, and best of all, you'll learn how to learn a language quickly. Ruby, Io, Prolog, Scala, Erlang, Clojure, Haskell. With Seven Languages in Seven Weeks, by Bruce A. Tate, you'll go beyond the syntax-and beyond the 20-minute tutorial you'll find someplace online. This book has an audacious goal: to present a meaningful exploration of seven languages within a single book. Rather than serve as a complete reference or installation guide, Seven Languages hits what's essential and unique about each language. Moreover, this approach will help teach you how to grok new languages. For each language, you'll solve a nontrivial problem, using techniques that show off the language's most important features. As the book proceeds, you'll discover the strengths and weaknesses of the languages, while dissecting the process of learning languages quickly--for example, finding the typing and programming models, decision structures, and how you interact with them. Among this group of seven, you'll explore the most critical programming models of our time. Learn the dynamic typing that makes Ruby, Python, and Perl so flexible and compelling. Understand the underlying prototype system that's at the heart of JavaScript. See how pattern matching in Prolog shaped the development of Scala and Erlang. Discover how pure functional programming in Haskell is different from the Lisp family of languages, including Clojure. Explore the concurrency techniques that are quickly becoming the backbone of a new generation of Internet applications. Find out how to use Erlang's let-it-crash philosophy for building fault-tolerant systems. Understand the actor model that drives concurrency design in Io and Scala. Learn how Clojure uses versioning to solve some of the most difficult concurrency problems. It's all here, all in one place. Use the concepts from one language to find creative solutions in another-or discover a language that may become one of your favorites.

    This wonderfully accessible book illuminates the professional and personal paths that led him to this remarkable conclusion.All interactions between particles in the universe, Lloyd explains, convey not only energy but also information—in other words, particles not only collide, they compute. And what is the entire universe computing, ultimately? “Its own dynamical evolution,” he says. “As the computation proceeds, reality unfolds.”To elucidate his theory, Lloyd examines the history of the cosmos, posing questions that in other hands might seem unfathomably complex: How much information is there in the universe? What information existed at the moment of the Big Bang and what happened to it? How do quantum mechanics and chaos theory interact to create our world? Could we attempt to re-create it on a giant quantum computer? Programming the Universe presents an original and compelling vision of reality, revealing our world in an entirely new light.

    Written by the man who started the adventure, Behind Deep Blue reveals the inside story of what happened behind the scenes at the two historic Deep Blue vs. Kasparov matches. This is also the story behind the quest to create the mother of all chess machines. The book unveils how a modest student project eventually produced a multimillion dollar supercomputer, from the development of the scientific ideas through technical setbacks, rivalry in the race to develop the ultimate chess machine, and wild controversies to the final triumph over the world's greatest human player.In nontechnical, conversational prose, Feng-hsiung Hsu, the system architect of Deep Blue, tells us how he and a small team of fellow researchers forged ahead at IBM with a project they'd begun as students at Carnegie Mellon in the mid-1980s: the search for one of the oldest holy grails in artificial intelligence--a machine that could beat any human chess player in a bona fide match. Back in 1949 science had conceived the foundations of modern chess computers but not until almost fifty years later--until Deep Blue--would the quest be realized.Hsu refutes Kasparov's controversial claim that only human intervention could have allowed Deep Blue to make its decisive, "uncomputerlike" moves. In riveting detail he describes the heightening tension in this war of brains and nerves, the "smoldering fire" in Kasparov's eyes. Behind Deep Blue is not just another tale of man versus machine. This fascinating book tells us how man as genius was given an ultimate, unforgettable run for his mind, no, not by the genius of a computer, but of man as toolmaker.

    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

    Learn them fast and learn them well. Have you always wanted to learn to build your own website but are afraid it'll be too difficult for you? Or perhaps you are a blogger who wants to tweak your blog's design, without having to spend money on an expensive theme. This book is for you. You no longer have to waste your time and money learning HTML and CSS from lengthy books, expensive online courses or complicated tutorials. What this book offers... HTML and CSS for Beginners Complex concepts are broken down into simple steps to ensure that you can easily master the two languages even if you have never coded before. Carefully Chosen Examples (with images) Examples are carefully chosen to illustrate all concepts. In addition, images are provided whenever necessary so that you can immediately see the visual effects of various CSS properties. Learn The Languages Fast Concepts are presented in a "to-the-point" style to cater to the busy individual. With this book, you can learn HTML and CSS in just one day and start coding immediately. How is this book different... The best way to learn programming is by doing. End-of-Chapter Exercises Each CSS chapter comes with an end-of-chapter exercise where you get to practice the different CSS properties covered in the chapter and see first hand how different CSS values affect the design of the website. Bonus Project The book also includes a bonus project that requires the application of all the HTML and CSS concepts taught previously. Working through the project will not only give you an immense sense of achievement, it’ll also help you see how the various concepts tie together. Are you ready to dip your toes into the exciting world of web development? This book is for you. Click the BUY button and download it now. What you'll learn: - What is CSS and HTML? - What software do you need to write and run CSS codes? - What are HTML tags and elements? - What are the commonly used HTML tags and how to use them? - What are HTML IDs and Classes? - What is the basic CSS syntax? - What are CSS selectors? - What are pseudo classes and pseudo elements? - How to apply CSS rules to your website and what is the order of precedence? - What is the CSS box model? - How to position and float your CSS boxes - How to hide HTML content - How to change the background of CSS boxes - How to use the CSS color property to change colors - How to modify text and font of a website - How to create navigation bars - How to create gorgeous looking tables to display your data .. and more... Click the BUY button and download the book now to start learning HTML and CSS now. Learn them fast and learn them well. Tags: ------------ CSS, HTML5, web development, web page design, CSS examples, CSS tutorials, CSS coding, CSS for Dummies

    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.

    “The result is a veritable pi

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    a personal confession of its creator and a kind of involuntary and unperceived memoir.". Stewart affirms this maxim in his colorful reinterpretation of the lives and works of 17th-century philosophers Spinoza and Leibniz. In November 1676, the foppish courtier Leibniz, "the ultimate insider... an orthodox Lutheran from conservative Germany," journeyed to The Hague to visit the self-sufficient, freethinking Spinoza, "a double exile... an apostate Jew from licentious Holland." A prodigious polymath, Leibniz understood Spinoza's insight that "science was in the process of rendering the God of revelation obsolete; that it had already undermined the special place of the human individual in nature." Spinoza embraced this new world. Seeing the orthodox God as a "prop for theocratic tyranny," he articulated the basic theory for the modern secular state. Leibniz, on the other hand, spent the rest of his life championing God and theocracy like a defense lawyer defending a client he knows is guilty. He elaborated a metaphysics that was, at bottom, a reaction to Spinoza and collapses into Spinozism, as Stewart deftly shows. For Stewart, Leibniz's reaction to Spinoza and modernity set the tone for "the dominant form of modern philosophy"—a category that includes Kant, Hegel, Bergson, Heidegger and "the whole 'postmodern' project of deconstructing the phallogocentric tradition of western thought." Readers of philosophy may find much to disagree with in these arguments, but Stewart's wit and profluent prose make this book a fascinating read.

    Never before in history have we been able to acquire, record, communicate, and use information in so many different forms. Never before have we had access to such vast quantities of data of every kind. This revolution goes far beyond the limitless content that fills our lives, because information also underlies our understanding of ourselves, the natural world, and the universe. It is the key that unites fields as different as linguistics, cryptography, neuroscience, genetics, economics, and quantum mechanics. And the fact that information bears no necessary connection to meaning makes it a profound puzzle that people with a passion for philosophy have pondered for centuries.Table of ContentsLECTURE 1The Transformability of Information 4LECTURE 2Computation and Logic Gates 17LECTURE 3Measuring Information 26LECTURE 4Entropy and the Average Surprise 34LECTURE 5Data Compression and Prefix-Free Codes 44LECTURE 6Encoding Images and Sounds 57LECTURE 7Noise and Channel Capacity 69LECTURE 8Error-Correcting Codes 82LECTURE 9Signals and Bandwidth 94LECTURE 10Cryptography and Key Entropy 110LECTURE 11Cryptanalysis and Unraveling the Enigma 119LECTURE 12Unbreakable Codes and Public Keys 130LECTURE 13What Genetic Information Can Do 140LECTURE 14Life’s Origins and DNA Computing 152LECTURE 15Neural Codes in the Brain 169LECTURE 16Entropy and Microstate Information 185LECTURE 17Erasure Cost and Reversible Computing 198LECTURE 18Horse Races and Stock Markets 213LECTURE 19Turing Machines and Algorithmic Information 226LECTURE 20Uncomputable Functions and Incompleteness 239LECTURE 21Qubits and Quantum Information 253LECTURE 22Quantum Cryptography via Entanglement 266LECTURE 23It from Bit: Physics from Information 281LECTURE 24The Meaning of Information 293