Build hands-on expertise through a series of lessons, exercises, and suggested practices—and help maximize your performance on the job.Provides in-depth, hands-on training you take at your own pace Focuses on job-role-specific expertise for using HTML5, JavaScript, and CSS3 to begin building modern web and Windows 8 apps Features pragmatic lessons, exercises, and practices Creates a foundation of skills which, along with on-the-job experience, can be measured by Microsoft Certification exams such as 70-480 Coverage includes: creating HTML5 documents; implementing styles with CSS3; JavaScript in depth; using Microsoft developer tools; AJAX; multimedia support; drawing with Canvas and SVG; drag and drop functionality; location-aware apps; web storage; offline apps; writing your first simple Windows 8 apps; and other key topics

    We have Mindstorms to thank for that. In this book, pioneering computer scientist Seymour Papert uses the invention of LOGO, the first child-friendly programming language, to make the case for the value of teaching children with computers. Papert argues that children are more than capable of mastering computers, and that teaching computational processes like de-bugging in the classroom can change the way we learn everything else. He also shows that schools saturated with technology can actually improve socialization and interaction among students and between students and teachers.

    You'll work through the development of each project, implementing and running the applications while learning new features along the way.Web frameworks are playing a major role in the creation of today's most compelling web applications, because they automate many of the tedious tasks, allowing developers to instead focus on providing users with creative and powerful features. Python developers have been particularly fortunate in this area, having been able to take advantage of Django, a very popular open source web framework whose stated goal is to make it easier to build better web applications more quickly with less code.Practical Django Projects is the first book to introduce this popular framework by way of a series of realworld projects. What you'll learn Capitalize upon Django's welldefined framework architecture to build web applications faster than ever before Learn by doing by working through the creation of three realworld projects, including a content management system, blog, and social networking site Build userfriendly web sites with wellstructured URLs, session tracking, and syndication options Let Django handle tedious tasks such as database interaction while you focus on building compelling applications Who this book is forWeb developers seeking to use the powerful Django framework to build powerful web sites. "

    The text relates the fundamentals to three real-world examples: Intel's 8085, Motorola's 6800, and the 6502 chip used by Apple Computers. This edition includes a student version of the TASM cross-assembler software program, experiments for Digital Computer Electronics and more.

    "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems."

    Stallings presents the nature and characteristics of modern-day operating systems clearly and completely.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    The authors are well-known for their clear presentation that makes the material accessible to a a broad audience and requires no special previous mathematical experience. KEY TOPICS: In this new edition, the authors incorporate a somewhat more informal, friendly writing style to present both classical and contemporary theories of computation. Algorithms, complexity analysis, and algorithmic ideas are introduced informally in Chapter 1, and are pursued throughout the book. Each section is followed by problems.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    The rapid maturation of PHP has created a skeptical population of users from more traditional enterprise languages who question the readiness and ability of PHP to scale, as well as a large population of PHP developers without formal computer science backgrounds who have learned through the hands-on experimentation while developing small and midsize applications in PHP. While there are many books on learning PHP and developing small applications with it, there is a serious lack of information on scaling PHP for large-scale, business-critical systems. Schlossnagle's Advanced PHP Programming fills that void, demonstrating that PHP is ready for enterprise Web applications by showing the reader how to develop PHP-based applications for maximum performance, stability, and extensibility.

    This well-respected text offers an accessible introduction to functional programming concepts and techniques for students of mathematics and computer science. The treatment is as nontechnical as possible, and it assumes no prior knowledge of mathematics or functional programming. Cogent examples illuminate the central ideas, and numerous exercises appear throughout the text, offering reinforcement of key concepts. All problems feature complete solutions.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    Hundreds of thousands of Python developers around the world rely on Python for general-purpose tasks, Internet scripting, systems programming, user interfaces, and product customization. Available on all major computing platforms, including commercial versions of Unix, Linux, Windows, and Mac OS X, Python is portable, powerful and remarkable easy to use.With its convenient, quick-reference format, "Python Pocket Reference," 3rd Edition is the perfect on-the-job reference. More importantly, it's now been refreshed to cover the language's latest release, Python 2.4. For experienced Python developers, this book is a compact toolbox that delivers need-to-know information at the flip of a page. This third edition also includes an easy-lookup index to help developers find answers fast!Python 2.4 is more than just optimization and library enhancements; it's also chock full of bug fixes and upgrades. And these changes are addressed in the "Python Pocket Reference," 3rd Edition. New language features, new and upgraded built-ins, and new and upgraded modules and packages--they're all clarified in detail.The "Python Pocket Reference," 3rd Edition serves as the perfect companion to "Learning Python" and "Programming Python."

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    But do you know how to use them to their greatest possible advantage? Only now, with the publication of Linkers & Loaders, is there an authoritative book devoted entirely to these deep-seated compile-time and run-time processes. The book begins with a detailed and comparative account of linking and loading that illustrates the differences among various compilers and operating systems. On top of this foundation, the author presents clear practical advice to help you create faster, cleaner code. You'll learn to avoid the pitfalls associated with Windows DLLs, take advantage of the space-saving, performance-improving techniques supported by many modern linkers, make the best use of the UNIX ELF library scheme, and much more. If you're serious about programming, you'll devour this unique guide to one of the field's least understood topics. Linkers & Loaders is also an ideal supplementary text for compiler and operating systems courses.