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.

    But developing these systems brings its own set of headaches. With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.Microservice technologies are moving quickly. Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You'll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain.Discover how microservices allow you to align your system design with your organization's goalsLearn options for integrating a service with the rest of your systemTake an incremental approach when splitting monolithic codebasesDeploy individual microservices through continuous integrationExamine the complexities of testing and monitoring distributed servicesManage security with user-to-service and service-to-service modelsUnderstand the challenges of scaling microservice architectures

    But with a typical price tag of $5,000 to $10,000 for a usability consultant to conduct each round of tests, it rarely happens. In this how-to companion to Don't Make Me Think: A Common Sense Approach to Web Usability, Steve Krug spells out an approach to usability testing that anyone can easily apply to their own web site, application, or other product. (As he said in Don't Make Me Think, "It's not rocket surgery".)In this new book, Steve explains how to: -Test any design, from a sketch on a napkin to a fully-functioning web site or application-Keep your focus on finding the most important problems (because no one has the time or resources to fix them all)-Fix the problems that you find, using his "The least you can do" approachBy pairing the process of testing and fixing products down to its essentials (A morning a month, that's all we ask ), Rocket Surgery makes it realistic for teams to test early and often, catching problems while it's still easy to fix them. Rocket Surgery Made Easy adds demonstration videos to the proven mix of clear writing, before-and-after examples, witty illustrations, and practical advice that made Don't Make Me Think so popular.

    This book explains the technical aspects of hacking, including stack based overflows, heap based overflows, string exploits, return-into-libc, shellcode, and cryptographic attacks on 802.11b.

    Vaughn Vernon couples guided approaches to implementation with modern architectures, highlighting the importance and value of focusing on the business domain while balancing technical considerations.Building on Eric Evans’ seminal book, Domain-Driven Design, the author presents practical DDD techniques through examples from familiar domains. Each principle is backed up by realistic Java examples–all applicable to C# developers–and all content is tied together by a single case study: the delivery of a large-scale Scrum-based SaaS system for a multitenant environment.The author takes you far beyond “DDD-lite” approaches that embrace DDD solely as a technical toolset, and shows you how to fully leverage DDD’s “strategic design patterns” using Bounded Context, Context Maps, and the Ubiquitous Language. Using these techniques and examples, you can reduce time to market and improve quality, as you build software that is more flexible, more scalable, and more tightly aligned to business goals.

    The book details the major subsystems and features of the Linux kernel, including its design, implementation, and interfaces. It covers the Linux kernel with both a practical and theoretical eye, which should appeal to readers with a variety of interests and needs. The author, a core kernel developer, shares valuable knowledge and experience on the 2.6 Linux kernel. Specific topics covered include process management, scheduling, time management and timers, the system call interface, memory addressing, memory management, the page cache, the VFS, kernel synchronization, portability concerns, and debugging techniques. This book covers the most interesting features of the Linux 2.6 kernel, including the CFS scheduler, preemptive kernel, block I/O layer, and I/O schedulers. The third edition of Linux Kernel Development includes new and updated material throughout the book:An all-new chapter on kernel data structuresDetails on interrupt handlers and bottom halvesExtended coverage of virtual memory and memory allocationTips on debugging the Linux kernelIn-depth coverage of kernel synchronization and lockingUseful insight into submitting kernel patches and working with the Linux kernel community

    Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you're not quite Google's size...yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests...thinking like real users...implementing exploratory, black box, white box, and acceptance testing...getting usable feedback...tracking issues...choosing and creating tools...testing "Docs & Mocks," interfaces, classes, modules, libraries, binaries, services, and infrastructure...reviewing code and refactoring...using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator-and make your whole organization more productive!

    As John invested in these skills his career took off, and he became a highly paid, highly sought-after developer and consultant. Today John helps more than 1.4 million programmers every year to increase their income by developing this unique blend of skills. "If you're a developer, green or a veteran, you owe it to yourself to read The Complete Software Developers Career Guide." - Jason Down, Platform Developer, Ontario, Canada What You Will Learn in This Book How to systematically find and fill the gaps in your technical knowledge so you can face any new challenge with confidence Should you take contract work - or hold out for a salaried position? Which will earn you more, what the tradeoffs are, and how your personality should sway your choice Should you learn JavaScript, C#, Python, C++? How to decide which programming language you should master first Ever notice how every job ever posted requires "3-5 years of experience," which you don't have? Simple solution for this frustrating chicken-and-egg problem that allows you to build legitimate job experience while you learn to code Is earning a computer science degree a necessity - or a total waste of time? How to get a college degree with maximum credibility and minimum debt Coding bootcampssome are great, some are complete scams. How to tell the difference so you don't find yourself cheated out of $10,000 Interviewer tells you, "Dress code is casual around here - the development team wears flipflops." What should you wear? How do you deal with a boss who's a micromanager. Plus how helping your manager with his goals can make you the MVP of your team The technical skills that every professional developer must have - but no one teaches you (most developers are missing some critical pieces, they don't teach this stuff in college, you're expected to just "know" this) An inside look at the recruiting industry. What that "friendly" recruiter really wants from you, how they get paid, and how to avoid getting pigeonholed into a job you'll hate Who Should Read This Book Entry-Level Developers This book will show you how to ensure you have the technical skills your future boss is looking for, create a resume that leaps off a hiring manager's desk, and escape the "no work experience" trap. Mid-Career Developers You'll see how to find and fill in gaps in your technical knowledge, position yourself as the one team member your boss can't live without, and turn those dreaded annual reviews into chance to make an iron-clad case for your salary bump. Senior Developers This book will show you how to become a specialist who can command above-market wages, how building a name for yourself can make opportunities come to you, and how to decide whether consulting or entrepreneurship are paths you should pursue.

    Every programmer can find uses for regular expressions, but their power doesn't come worry-free. Even seasoned users often suffer from poor performance, false positives, false negatives, or perplexing bugs. Regular Expressions Cookbook offers step-by-step instructions for some of the most common tasks involving this tool, with recipes for C#, Java, JavaScript, Perl, PHP, Python, Ruby, and VB.NET.With this book, you will:Understand the basics of regular expressions through a concise tutorial Use regular expressions effectively in several programming and scripting languages Learn how to validate and format input Manage words, lines, special characters, and numerical values Find solutions for using regular expressions in URLs, paths, markup, and data exchange Learn the nuances of more advanced regex features Understand how regular expressions' APIs, syntax, and behavior differ from language to language Write better regular expressions for custom needs Whether you're a novice or an experienced user, Regular Expressions Cookbook will help deepen your knowledge of this unique and irreplaceable tool. You'll learn powerful new tricks, avoid language-specific gotchas, and save valuable time with this huge library of proven solutions to difficult, real-world problems.

    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

    because the language seems like a good way to accomplish programming tasks that don't require the low-level bit handling power of C.-- Richard Bejtlich, TaoSecurityPython is a new and innovative scripting language. It is set to replace Perl as the programming language of choice for shell scripters, and for serious application developers who want a feature-rich, yet simple language to deploy their products.Dive Into Python is a hands-on guide to the Python language. Each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end.This is the perfect resource for you if you like to jump into languages fast and get going right away. If you're just starting to learn Python, first pick up a copy of Magnus Lie Hetland's Practical Python.

    

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

    It has been winning converts from dynamic language enthusiasts as well as users of traditional compiled languages. The former appreciate the robustness and efficiency that Go's lightweight type system brings to their code; the latter find Go's simplicity and fast tools a refreshing change. Thanks to its well-designed standard libraries and its excellent support for concurrent programming, Go is fast becoming the language of choice for distributed systems. The Go Programming Language is the definitive book on Go for the working programmer. It assumes no prior knowledge of Go, nor any other specific programming language, so you'll find it an accessible guide whether you come from JavaScript, Ruby, Python, Java, or C++. The book will quickly get you started using Go effectively from the beginning, and by the end, you will know how to use it well to write clear, idiomatic and efficient programs to solve real-world problems. You'll understand not just how to use its standard libraries, but how they work, and how to apply the same design techniques to your own projects. The earlier chapters will introduce you to the basic concepts of Go programming---numbers, strings, functions---while at the same time presenting important computer science concepts like recursion, and useful examples of graphics, UTF-8, and error handling. The chapters on methods and interfaces will show you a new way to think about object-oriented programming; the chapter on concurrency explains why concurrency is so important in modern programming, and how Go helps you handle it well. You'll also learn about Go's pragmatic but effective approach to testing; how to build, test, and manage projects using the go tool, and the art of metaprogramming using reflection. The book contains hundreds of interesting and practical examples that cover the whole language and a wide range of applications. The code samples from the book are available for download from gopl.io.

    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