Learn C programming from one of the best. Stephen Kochan’s Programming in C is thorough with easy-to-follow instructions that are sure to benefit beginning programmers. In its third edition, the style in this book remains true to the simple, instructional style of previous editions. It provides you with updated and relevant examples of how C programming can be used with small, fast programs, similar to the programming used by large game developers such as Nintendo. If you want a one-stop-source for C programming, this book is it!

Programming in C, Third Edition is a revised edition of a classic programming title. Author Stephen Kochan's style and thorough explanations have earned him a place among the most respected of computer book authors. Although the C programming language hasn't undergone any major changes, it's enjoying new life among game programmers and small device programmers, where its simple elegance makes it the ideal choice for small fast programs. Large game developers, such as Nintendo, use C almost exclusively. This edition combines the time-tested instructional style of Stephen Kochan with updated and relevant examples.

Programming in Objective-C is a concise, carefully written tutorial on the basics of Objective-C and object-oriented programming. The book makes no assumption about prior experience with object-oriented programming languages or with the C language (upon which Objective-C is based). And because of this, both novice and experienced programmers alike can use this book to quickly and effectively learn the fundamentals of Objective-C. Readers can also learn the concepts of object-oriented programming without having to first learn all of the intricacies of the underlying procedural language (C). This approach, combined with many small program examples and exercises at the end of each chapter, makes it ideally suited for either classroom use or self-study. Growth is expected in this language. At the January 2003 MacWorld, it was announced that there are 5 million Mac OS X users and each of their boxes ships with Objective-C built in.

A complete introduction to the Objective-C language for Mac OS X and iPhone development

Objective-C has become the standard programming language for application development on the Mac OS X and iPhone platforms. A powerful yet simple object-oriented programming language that’s based on the C programming language, Objective-C is widely available not only on OS X but across many operating systems that support the gcc compiler, including Linux, Unix, and Windows systems.

Programming in Objective-C 2.0 provides the new programmer a complete, step-by-step introduction to the Objective-C language. The book does not assume previous experience with either C or object-oriented programming languages, and it includes many detailed, practical examples of how to put Objective-C to use in your everyday programming needs.

The second edition of this book has been updated and expanded to cover Objective-C 2.0. It shows not only how to take advantage of the Foundation framework’s rich built-in library of classes but also how to use the iPhone SDK to develop programs designed specifically for the iPhone and iPod Touch.

“The best book on any programming language that I’ve ever read. If you want to learn Objective-C, buy it.”

–Calvin Wolcott

“An excellent resource for a new programmer who wants to learn Objective-C as their first programming language–a woefully underserved market.” –Pat Hughes

Stephen G. Kochan is author or coauthor of several bestselling books on the C language, including Programming in C, Programming in ANSI C, and Topics in C Programming. He has written extensively on Unix and is author of Exploring the Unix System and Unix Shell Programming. Kochan has been programming Macintosh computers since the introduction of the first Mac in 1984 and wrote Programming C for the Mac for the Apple Press Library as well as Beginning AppleScript (Wrox).

Programming Languages / Objective-C 2.0

$44.99 US / $48.99 CANADA / £28.99 Net UK

The best single-source guide available for detailed treatment of advanced C programming for the UNIX environment. No other book addresses, in comparable depth, the topics explored here by authors Kochan and Wood—topics like X-Windows, pointers and structures, generating programs with "make," and debugging C programs.

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problemsAbout This Book Understand the principles of Bayesian Inference with less mathematical equationsLearn state-of-the art Machine Learning methodsFamiliarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guideWho This Book Is For

This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R. What You Will Learn Set up the R environmentCreate a classification model to predict and explore discrete variablesGet acquainted with Probability Theory to analyze random eventsBuild Linear Regression modelsUse Bayesian networks to infer the probability distribution of decision variables in a problemModel a problem using Bayesian Linear Regression approach with the R package BLRUse Bayesian Logistic Regression model to classify numerical dataPerform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computingIn Detail

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.

Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.

Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.

The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks. Style and approach

The book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.

Leverage Swift and enhance your code to take your applications to the next levelAbout This Book Build solid, high performance applications in SwiftIncrease your efficiency by getting to grips with concurrency and parallel programmingUse Swift to design performance-oriented solutionsWho This Book Is For

This book is aimed at experienced Swift developers wanting to optimize their programs on Apple platforms to optimize application performance. What You Will Learn Build solid, stable, and reliable applications using SwiftUse REPL and Pl to manage and configure relational databasesExplore Swift's features including its static type system, value objects, and functional programming Design reusable code for high performance in SwiftUse to Xcode LLBD and REPL to debug commandsAvoid sharing resources by using concurrency and parallel programmingUnderstand the lazy loading pattern, lazy sequences, and lazy evolution.In Detail

Swift is one of the most popular and powerful programming languages for building iOS and Mac OS applications, and continues to evolve with new features and capabilities. Swift is considered a replacement to Objective-C and has performance advantages over Objective-C and Python. Swift adopts safe programming patterns and adds modern features to make programming easier, more flexible, and more fun.

Develop Swift and discover best practices that allow you to build solid applications and optimize their performance.

First, a few of performance characteristics of Swift will be explained. You will implement new tools available in Swift, including Playgrounds and REPL. These will improve your code efficiency, enable you to analyse Swift code, and enhance performance. Next, the importance of building solid applications using multithreading concurrency and multi-core device architecture is covered, before moving on to best practices and techniques that you should utilize when building high performance applications, such as concurrency and lazy-loading. Finally, you will explore the underlying structure of Swift further, and learn how to disassemble and compile Swift code. Style and approach

This is a comprehensive guide to enhancing Swift programming techniques and methodology to enable faster application development.

Computer Vision Metrics: Survey, Taxonomy, and Analysis provides a technical tour through computer vision, with a survey of nearly 100 types of local, regional, and global feature descriptors, blending history of the field with state-of-the-art analysis of contemporary methods, rather than just another how-to book with source code shortcuts and performance analysis. Observations are provided to develop intuition behind the methods and mathematics, interesting questions are raised for future research rather than providing all the answers, and a Vision Taxonomy is suggested to draw a conceptual map of the field. Extensive illustrations are included, with over 540 references to the literature in the comprehensive bibliography to dig deeper.

Computer Vision Metrics explores the key questions behind the design and mathematics of computer vision metrics and feature descriptors, providing a comprehensive survey and taxonomy of what methods are used, with analysis and observations about why the methods work. Several 3D depth sensing methods are surveyed including MVS, stereo, and structured light.

This work focuses on a slice through the field from the view of feature description metrics, or how to describe, compute, and design the macro-features and micro-features
that make up larger objects in images. The focus is on the pixel-side of the vision pipeline, with a light introduction to the back-end training, classification, machine learning, and matching stages. Computer Vision Metrics is written for engineers, scientists, and academic researchers in areas including video analytics, scene understanding, machine vision, face recognition, gesture recognition, pattern recognition, general object analysis, media processing, and computational photography.

What you’ll learn Current status, brief history, and future directions for computer vision metricsTaxonomy of local binary, gradient & other spectra, shape features,
and basis spacesOverview of 2D image sensing, 3D depth sensing, and image preprocessingVision pipeline optimization methods for computer vision applicationsCharacterization of ten OpenCV detectors using synthetic feature alphabets
Who this book is for

Engineers, scientists, and academic researchers in areas including media processing, computational photography, video analytics, scene understanding, machine vision, face recognition, gesture recognition, pattern recognition and general object analysis.
Table of Contents

Chapter 1. Image Capture and Representation

Chapter 2. Image Pre-Processing

Chapter 3. Global and Regional Features

Chapter 4. Local Feature Design Concepts, Classification, and Learning

Chapter 5. Taxonomy Of Feature Description Attributes

Chapter 6. Interest Point Detector and Feature Descriptor Survey

Chapter 7. Ground Truth Data, Data, Metrics, and Analysis

Chapter 8. Vision Pipelines and Optimizations

Appendix A. Synthetic Feature Analysis

Appendix B. Survey of Ground Truth Datasets

Appendix C. Imaging and Computer Vision Resources

Appendix D. Extended SDM Metrics

With WordPress, anyone can build an optimized website with the least amount of effort possible and then make it available to the world in no time.

This book will serve as a practical guide for everyone who intends to become an online publisher, website owner, or even a website developer. Beginning with the basic features of WordPress, the book lays a solid foundation to deal with advanced and complex features. It then moves on to helping you choose and install various themes.

Gradually, with increasing complexity, the book goes into the development of your own themes, acting as a beginner's guide to theme and plugin development.

Concluding the learning curve with miscellaneous tasks such as community blogging and administrating the established site, this book empowers you with the ability to maintain your site.

The Mac has fully embraced OpenGL throughout its visual systems. In fact, Apple's highly efficient, modern OpenGL implementation makes Mac OS X one of today's best platforms for OpenGL development. OpenGL® Programming on Mac OS® X is the first comprehensive resource for every graphics programmer who wants to create, port, or optimize OpenGL applications for this high-volume platform.

Leading OpenGL experts Robert Kuehne and J. D. Sullivan thoroughly explain the Mac's diverse OpenGL APIs, both old and new. They illuminate crucial OpenGL setup, configuration, and performance issues that are unique to the Mac platform. Next, they offer practical, start-to-finish guidance for integrating key Mac-native APIs with OpenGL, and leveraging the full power of the Mac platform in your graphics applications.

Coverage includes A thorough review of Mac hardware and software architectures and their performance implicationsIn-depth, expert guidance for accessing OpenGL from each of the Mac's core APIs: CGL, AGL, and CocoaInteroperating with other Mac APIs: incorporating video with QuickTime, performing image effects with Core Image, and processing CoreVideo dataAnalyzing Mac OpenGL application performance, resolving bottlenecks, and leveraging optimizations only available on the MacDetecting, integrating, and using OpenGL extensionsAn accompanying Web site (www.macopenglbook.com) contains the book's example code, plus additional OpenGL-related resources.

OpenGL® Programming on Mac OS® X will be valuable to Mac programmers seeking to leverage OpenGL's power, OpenGL developers porting their applications to the Mac platform, and cross-platform graphics developers who want to take advantage of the Mac platform's uniquely intuitive style and efficiency.

This practically-oriented textbook presents an accessible introduction to discrete mathematics through a substantial collection of classroom-tested exercises. Each chapter opens with concise coverage of the theory underlying the topic, reviewing the basic concepts and establishing the terminology, as well as providing the key formulae and instructions on their use. This is then followed by a detailed account of the most common problems in the area, before the reader is invited to practice solving such problems for themselves through a varied series of questions and assignments. Topics and features: provides an extensive set of exercises and examples of varying levels of complexity, suitable for both laboratory practical training and self-study; offers detailed solutions to many problems, applying commonly-used methods and computational schemes; introduces the fundamentals of mathematical logic, the theory of algorithms, Boolean algebra, graph theory, sets, relations, functions, and combinatorics; presents more advanced material on the design and analysis of algorithms, including asymptotic analysis, and parallel algorithms; includes reference lists of trigonometric and finite summation formulae in an appendix, together with basic rules for differential and integral calculus.

This hands-on study guide is designed to address the core needs of undergraduate students training in computer science, informatics, and electronic engineering, emphasizing the skills required to develop and implement an algorithm in a specific programming language.