From extended integer types and compound literals to Boolean support and variable-length arrays, this book helps you learn to create practical and real-world applications with C programming. It contains review questions and programming exercises.

    The principal enhancement in Java 8 was the addition of functional programming constructs to Java's object-oriented roots. Java 7, 8, and 9 also introduced language features, such as the try-with-resources statement, the diamond operator for generic types, default and static methods in interfaces, the @SafeVarargs annotation, and modules. New library features include pervasive use of functional interfaces and streams, the java.time package for manipulating dates and times, and numerous minor enhancements such as convenience factory methods for collections. In this new edition of Effective Java, Bloch updates the work to take advantage of these new language and library features, and provides specific best practices for their use. Java's increased support for multiple paradigms increases the need for best-practices advice, and this book delivers. As in previous editions, each chapter consists of several "items," each presented in the form of a short, standalone essay that provides specific advice, insight into Java platform subtleties, and updated code examples. The comprehensive descriptions and explanations for each item illuminate what to do, what not to do, and why. Coverage includes:Updated techniques and best practices on classic topics, including objects, classes, methods, libraries, and generics How to avoid the traps and pitfalls of commonly misunderstood subtleties of the platform Focus on the language and its most fundamental libraries, such as java.lang and java.util

    You'll master the Unity toolset from the ground up, adding the skills you need to go from application coder to game developer. Based on Unity version 5.About the BookThis book helps readers build successful games with the Unity game development platform. You will use the powerful C# language, Unity's intuitive workflow tools, and a state-of-the-art rendering engine to build and deploy mobile, desktop, and console games. Unity's single codebase approach minimizes inefficient switching among development tools and concentrates your attention on making great interactive experiences.Unity in Action teaches you how to write and deploy games. You'll master the Unity toolset from the ground up, adding the skills you need to go from application coder to game developer. Each sample project illuminates specific Unity features and game development strategies. As you read and practice, you'll build up a well-rounded skill set for creating graphically driven 2D and 3D game applications.You'll need to know how to program, in C# or a similar OO language. No previous Unity experience or game development knowledge is assumed.

    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.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    Best-selling author D.S. Malik employs a student-focused approach, using complete programming examples to teach introductory programming concepts. This third edition has been enhanced to further demonstrate the use of OOD methodology, to introduce sorting algorithms (bubble sort and insertion sort), and to present additional material on abstract classes. In addition, the exercise sets at the end of each chapter have been expanded, and now contain several calculus and engineering-related exercises. Finally, all programs have been written, compiled, and quality-assurance tested with Microsoft Visual C++ .NET, available as an optional compiler with this text.

    Now in a new Ninth Edition, this reader-friendly book has been completely revised and improved to ensure that the learning experience is enhanced. It's built on the strength of Irwin's problem-solving methodology, providing readers with a strong foundation as they advance in the field.

    Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.

    But if code isn't clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn't have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship . Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code on the fly into a book that will instill within you the values of a software craftsman and make you a better programmer but only if you work at it. What kind of work will you be doing? You'll be reading code - lots of code. And you will be challenged to think about what's right about that code, and what's wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code - of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and "smells" gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding ‣ How to tell the difference between good and bad code‣ How to write good code and how to transform bad code into good code‣ How to create good names, good functions, good objects, and good classes‣ How to format code for maximum readability ‣ How to implement complete error handling without obscuring code logic ‣ How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

    Try out more than 1,000 commands to find and get software, monitor system health and security, and access network resources. Then, apply the skills you learn from this book to use and administer desktops and servers running Ubuntu, Debian, and KNOPPIX or any other Linux distribution.

    In-depth explorations of the derivative, the differentiation and integration of the powers of x, and theorems on differentiation and antidifferentiation lead to a definition of the chain rule and examinations of trigonometric functions, logarithmic and exponential functions, techniques of integration, polar coordinates, much more. Clear-cut explanations, numerous drills, illustrative examples. 1967 edition. Solution guide available upon request.

    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.

    

    It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.A book by Git experts to turn you into a Git expert. Introduces the world of distributed version control Shows how to build a Git development workflow.

    The author has made this edition more accessible to better meet the needs of today's undergraduate mathematics and philosophy students. It is intended for the reader who has not studied logic previously, but who has some experience in mathematical reasoning. Material is presented on computer science issues such as computational complexity and database queries, with additional coverage of introductory material such as sets.