Understanding Computation explains theoretical computer science in a context you’ll recognize, helping you appreciate why these ideas matter and how they can inform your day-to-day programming.Rather than use mathematical notation or an unfamiliar academic programming language like Haskell or Lisp, this book uses Ruby in a reductionist manner to present formal semantics, automata theory, and functional programming with the lambda calculus. It’s ideal for programmers versed in modern languages, with little or no formal training in computer science.* Understand fundamental computing concepts, such as Turing completeness in languages* Discover how programs use dynamic semantics to communicate ideas to machines* Explore what a computer can do when reduced to its bare essentials* Learn how universal Turing machines led to today’s general-purpose computers* Perform complex calculations, using simple languages and cellular automata* Determine which programming language features are essential for computation* Examine how halting and self-referencing make some computing problems unsolvable* Analyze programs by using abstract interpretation and type systems

    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

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    What makes an operating system modern? According to author Andrew Tanenbaum, it is the awareness of high-demand computer applications--primarily in the areas of multimedia, parallel and distributed computing, and security. The development of faster and more advanced hardware has driven progress in software, including enhancements to the operating system. It is one thing to run an old operating system on current hardware, and another to effectively leverage current hardware to best serve modern software applications. If you don't believe it, install Windows 3.0 on a modern PC and try surfing the Internet or burning a CD. Readers familiar with Tanenbaum's previous text, Operating Systems, know the author is a great proponent of simple design and hands-on experimentation. His earlier book came bundled with the source code for an operating system called Minux, a simple variant of Unix and the platform used by Linus Torvalds to develop Linux. Although this book does not come with any source code, he illustrates many of his points with code fragments (C, usually with Unix system calls). The first half of Modern Operating Systems focuses on traditional operating systems concepts: processes, deadlocks, memory management, I/O, and file systems. There is nothing groundbreaking in these early chapters, but all topics are well covered, each including sections on current research and a set of student problems. It is enlightening to read Tanenbaum's explanations of the design decisions made by past operating systems gurus, including his view that additional research on the problem of deadlocks is impractical except for "keeping otherwise unemployed graph theorists off the streets." It is the second half of the book that differentiates itself from older operating systems texts. Here, each chapter describes an element of what constitutes a modern operating system--awareness of multimedia applications, multiple processors, computer networks, and a high level of security. The chapter on multimedia functionality focuses on such features as handling massive files and providing video-on-demand. Included in the discussion on multiprocessor platforms are clustered computers and distributed computing. Finally, the importance of security is discussed--a lively enumeration of the scores of ways operating systems can be vulnerable to attack, from password security to computer viruses and Internet worms. Included at the end of the book are case studies of two popular operating systems: Unix/Linux and Windows 2000. There is a bias toward the Unix/Linux approach, not surprising given the author's experience and academic bent, but this bias does not detract from Tanenbaum's analysis. Both operating systems are dissected, describing how each implements processes, file systems, memory management, and other operating system fundamentals. Tanenbaum's mantra is simple, accessible operating system design. Given that modern operating systems have extensive features, he is forced to reconcile physical size with simplicity. Toward this end, he makes frequent references to the Frederick Brooks classic The Mythical Man-Month for wisdom on managing large, complex software development projects. He finds both Windows 2000 and Unix/Linux guilty of being too complicated--with a particular skewering of Windows 2000 and its "mammoth Win32 API." A primary culprit is the attempt to make operating systems more "user-friendly," which Tanenbaum views as an excuse for bloated code. The solution is to have smart people, the smallest possible team, and well-defined interactions between various operating systems components. Future operating system design will benefit if the advice in this book is taken to heart. --Pete Ostenson

    Whether you are a beginner or an experienced administrator, you'll quickly be able to apply its principles and advice to your everyday problems.The book approaches Unix system administration from the perspective of your job -- the routine tasks and troubleshooting that make up your day. Whether you're dealing with frustrated users, convincing an uncomprehending management that you need new hardware, rebuilding the kernel, or simply adding new users, you'll find help in this book. You'll also learn about back up and restore and how to set up printers, secure your system, and perform many other system administration tasks. But the book is not for full-time system administrators alone. Linux users and others who administer their own systems will benefit from its practical, hands-on approach.This second edition has been updated for all major Unix platforms, including SunOS 4.1, Solaris 2.4, AIX 4.1, Linux 1.1, Digital Unix, OSF/1, SCO Unix Version 3, HP/UX Versions 9 and 10, and IRIX Version 6. The entire book has been thoroughly reviewed and tested on all of the platforms covered. In addition, networking, electronic mail, security, and kernel configuration topics have been expanded substantially.Topics covered include:Starting up and shutting down your system Adding new users Managing processes System security Organizing and planning file systems Planning and performing backups Setting up pointers TCP/IP networking Setting up email Adding terminals and disk drives Setting up and using the accounting system

    The third edition has been expanded to include "direct from the frontlines" coverage of Red Hat Linux. UNIX System Administration Handbook describes every aspect of system administration - from basic topics to UNIX esoterica - and provides explicit cover of four popular UNIX systems:- Red Hat Linux- Solaris- HP-UX- FreeBSDThis book stresses a practical approach to system administration. It's packed with war stories and pragmatic advice, not just theory and watered-down restatements of the manuals. Difficult subjects such as sendmail, kernel building, and DNS configuration are tackled head-on. Examples are provided for all four versions of UNIX and are drawn from real-life systems - warts and all.

    Why is this open-source tool so popular? Because it automates a tedious task: persisting your Java objects to a relational database. The inevitable mismatch between your object-oriented code and the relational database requires you to write code that maps one to the other. This code is often complex, tedious and costly to develop. Hibernate does the mapping for you.Not only that, Hibernate makes it easy. Positioned as a layer between your application and your database, Hibernate takes care of loading and saving of objects. Hibernate applications are cheaper, more portable, and more resilient to change. And they perform better than anything you are likely to develop yourself."Hibernate in Action" carefully explains the concepts you need, then gets you going. It builds on a single example to show you how to use Hibernate in practice, how to deal with concurrency and transactions, how to efficiently retrieve objects and use caching.The authors created Hibernate and they field questions from the Hibernate community every day-they know how to make Hibernate sing. Knowledge and insight seep out of every pore of this book."What's Inside"- ORM concepts- Getting started- Many real-world tasks- The Hibernate application development process

    Using playing cards, a newspaper, the back of an envelope, a Sudoku, some pennies and of course a pair of socks, Rob Eastaway shows how maths can demonstrate its secret beauties in even the most mundane of everyday objects. Among the many fascinating curiosities in these pages, you will discover the strange link between limericks and rabbits, an apparently 'fair' coin game where the odds are massively in your favour, why tourist boards can't agree on where the centre of Britain is, and how simple paper folding can lead to a Jurassic Park monster. With plenty of ideas you'll want to test out for yourself, this engaging and refreshing look at mathematics is for everyone.

    K. Dewdney's The Turing Omnibus.Updated and expanded, The Turing Omnibus offers 66 concise, brilliantly written articles on the major points of interest in computer science theory, technology, and applications. New for this tour: updated information on algorithms, detecting primes, noncomputable functions, and self-replicating computers--plus completely new sections on the Mandelbrot set, genetic algorithms, the Newton-Raphson Method, neural networks that learn, DOS systems for personal computers, and computer viruses.Contents:1 Algorithms 2 Finite Automata 3 Systems of Logic 4 Simulation 5 Godel's Theorem 6 Game Trees 7 The Chomsky Hierarchy 8 Random Numbers 9 Mathematical Research 10 Program Correctness 11 Search Trees 12 Error-Corecting Codes 13 Boolean Logic 14 Regular Languages 15 Time and Space Complexity 16 Genetic Algorithms 17 The Random Access Machine 18 Spline Curves 19 Computer Vision 20 Karnaugh Maps 21 The Newton-Raphson Method 22 Minimum Spanning Trees 23 Generative Grammars 24 Recursion 25 Fast Multiplication 26 Nondeterminism 27 Perceptrons 28 Encoders and Multiplexers 29 CAT Scanning 30 The Partition Problem 31 Turing Machines 32 The Fast Fourier Transform 33 Analog Computing 34 Satisfiability 35 Sequential Sorting 36 Neural Networks That Learn 37 Public Key Cryptography 38 Sequential Cirucits 39 Noncomputerable Functions 40 Heaps and Merges 41 NP-Completeness 42 Number Systems for Computing 43 Storage by Hashing 44 Cellular Automata 45 Cook's Theorem 46 Self-Replicating Computers 47 Storing Images 48 The SCRAM 49 Shannon's Theory 50 Detecting Primes 51 Universal Turing Machines 52 Text Compression 53 Disk Operating Systems 54 NP-Complete Problems 55 Iteration and Recursion 56 VLSI Computers 57 Linear Programming 58 Predicate Calculus 59 The Halting Problem 60 Computer Viruses 61 Searching Strings 62 Parallel Computing 63 The Word Problem 64 Logic Programming 65 Relational Data Bases 66 Church's Thesis

    There can be so much information available that you can't even decide where to start. Or worse, you start down the path of learning and quickly discover too many concepts, commands, and nuances that aren't explained. This kind of experience is frustrating and leaves you with more questions than answers.Python Programming for Beginners doesn't make any assumptions about your background or knowledge of Python or computer programming. You need no prior knowledge to benefit from this book. You will be guided step by step using a logical and systematic approach. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy for anyone to understand. Here is what you will learn by reading Python Programming for Beginners: When to use Python 2 and when to use Python 3. How to install Python on Windows, Mac, and Linux. Screenshots included. How to prepare your computer for programming in Python. The various ways to run a Python program on Windows, Mac, and Linux. Suggested text editors and integrated development environments to use when coding in Python. How to work with various data types including strings, lists, tuples, dictionaries, booleans, and more. What variables are and when to use them. How to perform mathematical operations using Python. How to capture input from a user. Ways to control the flow of your programs. The importance of white space in Python. How to organize your Python programs -- Learn what goes where. What modules are, when you should use them, and how to create your own. How to define and use functions. Important built-in Python functions that you'll use often. How to read from and write to files. The difference between binary and text files. Various ways of getting help and find Python documentation. Much more... Every single code example in the book is available to download, providing you with all the Python code you need at your fingertips! Scroll up, click the Buy Now With 1 Click button and get started learning Python today!

    With jokes, puns, and a rigorous problem solving based approach. You can download all the code samples used in the book from here: http://www.robmiles.com/s/Yellow-Book...

    This friendly, easy-to-read guide gently introduces you to the most commonly used features of SAS software plus a whole lot more! Authors Lora Delwiche and Susan Slaughter have revised the text to include concepts of the Output Delivery System; the STYLE= option in the PRINT, REPORT, and TABULATE procedures; ODS HTML, RTF, PRINTER, and OUTPUT destinations; PROC REPORT; more on PROC TABULATE; exporting data; and the colon modifier for informats. You'll find clear and concise explanations of basic SAS concepts (such as DATA and PROC steps), inputting data, modifying and combining data sets, summarizing and presenting data, basic statistical procedures, and debugging SAS programs. Each topic is presented in a self-contained, two-page layout complete with examples and graphics. This format enables new users to get up and running quickly, while the examples allow you to type in the program and see it work!

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.