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

    … The book the National Security Agency wanted never to be published." –Wired Magazine "…monumental… fascinating… comprehensive… the definitive work on cryptography for computer programmers…" –Dr. Dobb's Journal"…easily ranks as one of the most authoritative in its field." —PC Magazine"…the bible of code hackers." –The Millennium Whole Earth CatalogThis new edition of the cryptography classic provides you with a comprehensive survey of modern cryptography. The book details how programmers and electronic communications professionals can use cryptography—the technique of enciphering and deciphering messages-to maintain the privacy of computer data. It describes dozens of cryptography algorithms, gives practical advice on how to implement them into cryptographic software, and shows how they can be used to solve security problems. Covering the latest developments in practical cryptographic techniques, this new edition shows programmers who design computer applications, networks, and storage systems how they can build security into their software and systems. What's new in the Second Edition? * New information on the Clipper Chip, including ways to defeat the key escrow mechanism * New encryption algorithms, including algorithms from the former Soviet Union and South Africa, and the RC4 stream cipher * The latest protocols for digital signatures, authentication, secure elections, digital cash, and more * More detailed information on key management and cryptographic implementations

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    The calls to ban it were more or less instant.Re:cyclists is the tale of what happened next, of how we have spent two centuries wheeling our way about town and country on bikes--or on two-wheeled things that vaguely resembled what we now call bikes. Michael Hutchinson picks his way through those 200 years, discovering how cycling became a kinky vaudeville act for Parisians, how it became an American business empire, and how it went on to find a unique home in the British Isles. He considers the penny-farthing riders exploring the abandoned and lonely coaching roads during the railway era, and the Victorian high-society cyclists of the 1890s bicycle craze--a time when no aristocratic house party was without bicycles and when the Prince of Wales used to give himself an illicit thrill on a weekday afternoon by watching the women's riding-school in the Royal Albert Hall.Re:cyclists looks at how cycling became the sport, the pastime and the social life of millions of ordinary people, how it grew and how it suffered through the 1960s and '70s, and how at the dawn of the twenty-first century it rose again, much changed but still ultimately just someone careering along on two wheels.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    You might think the problem is your brain. It seems to have a mind of its own, a mind that doesn't always want to take in the dry, technical stuff you're forced to study. The fact is your brain craves novelty. It's constantly searching, scanning, waiting for something unusual to happen. After all, that's the way it was built to help you stay alive. It takes all the routine, ordinary, dull stuff and filters it to the background so it won't interfere with your brain's real work--recording things that matter. How does your brain know what matters? It's like the creators of the Head First approach say, suppose you're out for a hike and a tiger jumps in front of you, what happens in your brain? Neurons fire. Emotions crank up. Chemicals surge. That's how your brain knows.And that's how your brain will learn Java. Head First Java combines puzzles, strong visuals, mysteries, and soul-searching interviews with famous Java objects to engage you in many different ways. It's fast, it's fun, and it's effective. And, despite its playful appearance, Head First Java is serious stuff: a complete introduction to object-oriented programming and Java. You'll learn everything from the fundamentals to advanced topics, including threads, network sockets, and distributed programming with RMI. And the new. second edition focuses on Java 5.0, the latest version of the Java language and development platform. Because Java 5.0 is a major update to the platform, with deep, code-level changes, even more careful study and implementation is required. So learning the Head First way is more important than ever. If you've read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. You'll see why people say it's unlike any other Java book you've ever read.By exploiting how your brain works, Head First Java compresses the time it takes to learn and retain--complex information. Its unique approach not only shows you what you need to know about Java syntax, it teaches you to think like a Java programmer. If you want to be bored, buy some other book. But if you want to understand Java, this book's for you.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    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.

    You want to learn HTML so you can finally create those web pages you've always wanted, so you can communicate more effectively with friends, family, fans, and fanatic customers. You also want to do it right so you can actually maintain and expand your Web pages over time, and so your web pages work in all the browsers and mobile devices out there. Oh, and if you've never heard of CSS, that's okay - we won't tell anyone you're still partying like it's 1999 - but if you're going to create Web pages in the 21st century then you'll want to know and understand CSS.Learn the real secrets of creating Web pages, and why everything your boss told you about HTML tables is probably wrong (and what to do instead). Most importantly, hold your own with your co-worker (and impress cocktail party guests) when he casually mentions how his HTML is now strict, and his CSS is in an external style sheet.With Head First HTML with CSS & XHTML, you'll avoid the embarrassment of thinking web-safe colors still matter, and the foolishness of slipping a font tag into your pages. Best of all, you'll learn HTML and CSS in a way that won't put you to sleep. If you've read a Head First book, you know what to expect: a visually-rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, this book will load HTML, CSS, and XHTML into your brain in a way that sticks.So what are you waiting for? Leave those other dusty books behind and come join us in Webville. Your tour is about to begin."Elegant design is at the core of every chapter here, each concept conveyed with equal doses of pragmatism and wit." --Ken Goldstein, Executive Vice President, Disney Online"This book is a thoroughly modern introduction to forward-looking practices in web page markup and presentation." --Danny Goodman, author of Dynamic HTML: The Definitive Guide"What used to be a long trial and error learning process has now been reduced neatly into an engaging paperback." --Mike Davidson, CEO, Newsvine, Inc."I love Head First HTML with CSS & XHTML--it teaches you everything you need to learn in a 'fun coated' format!" --Sally Applin, UI Designer and Artist"I haven't had as much fun reading a book (other than Harry Potter) in years. And your book finally helped me break out of my hapless so-last-century way of creating web pages." --Professor David M. Arnow, Department of Computer and Information Science, Brooklyn College"If you've ever had a family member who wanted you to design a website for them, buy them Head First HTML with CSS and XHTML. If you've ever asked a family member to design you a web site, buy this book. If you've ever bought an HTML book and ended up using it to level your desk, or for kindling on a cold winter day, buy this book. This is the book you've been waiting for. This is the learning system you've been waiting for." --Warren Kelly, Blogcritics.org

    Now its founder, Reshma Saujani, wants to inspire you to be a girl who codes!Bursting with dynamic artwork, down-to-earth explanations of coding principles, and real-life stories of girls and women working at places like Pixar and NASA, this graphically animated book shows what a huge role computer science plays in our lives and how much fun it can be. No matter your interest—sports, the arts, baking, student government, social justice—coding can help you do what you love and make your dreams come true.Whether you’re a girl who’s never coded before, a girl who codes, or a parent raising one, this entertaining book, printed in bold two-color and featuring art on every page, will have you itching to create your own apps, games, and robots to make the world a better place.

      Do you remember your first visit to where the wild things are? How about curling up for hours on end to discover the secret of the Sorcerer’s Stone? Combining clear, practical advice with inspiration, wisdom, tips, and curated reading lists, How to Raise a Reader shows you how to instill the joy and time-stopping pleasure of reading.   Divided into four sections, from baby through teen, and each illustrated by a different artist, this book offers something useful on every page, whether it’s how to develop rituals around reading or build a family library, or ways to engage a reluctant reader. A fifth section, “More Books to Love: By Theme and Reading Level,” is chockful of expert recommendations. Throughout, the authors debunk common myths, assuage parental fears, and deliver invaluable lessons in a positive and easy-to-act-on way.

    Objects in harmonic motion have the ability to transfer some of their energy over large distances. Light Nature: This chapter covers the nature of light, polarization, and color.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    A dirty little secret assembly language programmers rarely admit to, however, is that what you really need to learn is machine organization, not assembly language programming. Write Great Code Vol I, the first in a series from assembly language expert Randall Hyde, dives right into machine organization without the extra overhead of learning assembly language programming at the same time. And since Write Great Code Vol I concentrates on the machine organization, not assembly language, the reader will learn in greater depth those subjects that are language-independent and of concern to a high level language programmer. Write Great Code Vol I will help programmers make wiser choices with respect to programming statements and data types when writing software, no matter which language they use.

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.