R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyLearning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

    Thoroughly updated to include Java 6, the Third Edition of Horstmann's bestselling text helps you absorb computing concepts and programming principles, develop strong problem-solving skills, and become a better programmer, all while exploring the elements of Java that are needed to write real-life programs. A top-notch introductory text for beginners, Big Java, Third Edition is also a thorough reference for students and professionals alike to Java technologies, Internet programming, database access, and many other areas of computer science.Features of the Third Edition: The 'Objects Gradual' approach leads you into object-oriented thinking step-by-step, from using classes, implementing simple methods, all the way to designing your own object-oriented programs. A strong emphasis on test-driven development encourages you to consider outcomes as you write programming code so you design better, more usable programs Helpful "Testing Track" introduces techniques and tools step by step, ensuring that you master one before moving on to the next New teaching and learning tools in WileyPLUS--including a unique assignment checker that enables you to test your programming problems online before you submit them for a grade Graphics topics are developed gradually throughout the text, conveniently highlighted in separate color-coded sections Updated coverage is fully compatible with Java 5 and includes a discussion of the latest Java 6 features

    Written by two of the best-known names in categorical logic, Conceptual Mathematics is the first book to apply categories to the most elementary mathematics. It thus serves two purposes: first, to provide a key to mathematics for the general reader or beginning student; and second, to furnish an easy introduction to categories for computer scientists, logicians, physicists, and linguists who want to gain some familiarity with the categorical method without initially committing themselves to extended study.

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    It is very readable and the stress many beginners face about memorizing arcane syntax typically presented by other programming languages will not affect you at all. Conversely, you will be able to concentrate on learning concepts and paradigms of programming. This book shall introduce you to an easy way to learn Python in just 7 days and in this time, be able to complete your own projects! By reading the book and implementing what you learn herein, you will realize just why major institutions like NASA, Google, Mozilla, Yahoo, Dropbox, IBM, Facebook and many others prefer to use python in their core products, services and business processes. Let

    This book will teach you how to use bash's advanced command-line features, such as command history, command-line editing, and command completion.This book also introduces shell programming,a skill no UNIX or Linus user should be without. The book demonstrates what you can do with bash's programming features. You'll learn about flow control, signal handling, and command-line processing and I/O. There is also a chapter on debugging your bash programs.Finally, Learning the bash Shell, Third Edition, shows you how to acquire, install, configure, and customize bash, and gives advice to system administrators managing bash for their user communities.This Third Edition covers all of the features of bash Version 3.0, while still applying to Versions 1.x and 2.x. It includes a debugger for the bash shell, both as an extended example and as a useful piece of working code. Since shell scripts are a significant part of many software projects, the book also discusses how to write maintainable shell scripts. And, of course, it discusses the many features that have been introduced to bash over the years: one-dimensional arrays, parameter expansion, pattern-matching operations, new commands, and security improvements.Unfailingly practical and packed with examples and questions for future study, Learning the bash Shell Third Edition is a valuable asset for Linux and other UNIX users.--back cover

    Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.

    

    The first edition of this book, Programming Perl, hit the shelves in 1990, and was quickly adopted as the undisputed bible of the language. Since then, Perl has grown with the times, and so has this book.Programming Perl is not just a book about Perl. It is also a unique introduction to the language and its culture, as one might expect only from its authors. Larry Wall is the inventor of Perl, and provides a unique perspective on the evolution of Perl and its future direction. Tom Christiansen was one of the first champions of the language, and lives and breathes the complexities of Perl internals as few other mortals do. Jon Orwant is the editor of The Perl Journal, which has brought together the Perl community as a common forum for new developments in Perl.Any Perl book can show the syntax of Perl's functions, but only this one is a comprehensive guide to all the nooks and crannies of the language. Any Perl book can explain typeglobs, pseudohashes, and closures, but only this one shows how they really work. Any Perl book can say that my is faster than local, but only this one explains why. Any Perl book can have a title, but only this book is affectionately known by all Perl programmers as "The Camel."This third edition of Programming Perl has been expanded to cover version 5.6 of this maturing language. New topics include threading, the compiler, Unicode, and other new features that have been added since the previous edition.

    When Ruby first burst onto the scene in the Western world, the Pragmatic Programmers were there with the definitive reference manual, Programming Ruby: The Pragmatic Programmer's Guide.Now in its second edition, author Dave Thomas has expanded the famous Pickaxe book with over 200 pages of new content, covering all the improved language features of Ruby 1.8 and standard library modules. The Pickaxe contains four major sections:An acclaimed tutorial on using Ruby.The definitive reference to the language.Complete documentation on all built-in classes, modules, and methodsComplete descriptions of all 98 standard libraries.If you enjoyed the First Edition, you'll appreciate the expanded content, including enhanced coverage of installation, packaging, documenting Ruby source code, threading and synchronization, and enhancing Ruby's capabilities using C-language extensions. Programming for the World Wide Web is easy in Ruby, with new chapters on XML/RPC, SOAP, distributed Ruby, templating systems, and other web services. There's even a new chapter on unit testing.This is the definitive reference manual for Ruby, including a description of all the standard library modules, a complete reference to all built-in classes and modules (including more than 250 significant changes since the First Edition). Coverage of other features has grown tremendously, including details on how to harness the sophisticated capabilities of irb, so you can dynamically examine and experiment with your running code. Ruby is a wonderfully powerful and useful language, and whenever I'm working with it this book is at my side --Martin Fowler, Chief Scientist, ThoughtWorks

    It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity. Topics include sets, logic, counting, methods of conditional and non-conditional proof, disproof, induction, relations, functions and infinite cardinality.

    Author Charles H. Roth, Jr. carefully presents the theory that is necessary for understanding the fundamental concepts of logic design while not overwhelming students with the mathematics of switching theory. Divided into 20 easy-to-grasp study units, the book covers such fundamental concepts as Boolean algebra, logic gates design, flip-flops, and state machines. By combining flip-flops with networks of logic gates, students will learn to design counters, adders, sequence detectors, and simple digital systems. After covering the basics, this text presents modern design techniques using programmable logic devices and the VHDL hardware description language.

    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.

    What they don't realize—and what Daniel Hillis's short book brilliantly demonstrates—is that computers' seemingly complex operations can be broken down into a few simple parts that perform the same simple procedures over and over again.Computer wizard Hillis offers an easy-to-follow explanation of how data is processed that makes the operations of a computer seem as straightforward as those of a bicycle. Avoiding technobabble or discussions of advanced hardware, the lucid explanations and colorful anecdotes in The Pattern on the Stone go straight to the heart of what computers really do.Hillis proceeds from an outline of basic logic to clear descriptions of programming languages, algorithms, and memory. He then takes readers in simple steps up to the most exciting developments in computing today—quantum computing, parallel computing, neural networks, and self-organizing systems.Written clearly and succinctly by one of the world's leading computer scientists, The Pattern on the Stone is an indispensable guide to understanding the workings of that most ubiquitous and important of machines: the computer.