By the end of the book, you'll understand how to develop a large, maintainable, and performant application with AngularJS.Guided by two engineers who worked on AngularJS at Google, you'll learn the components needed to build data-driven applications, using declarative programming and the Model-view-controller pattern. You'll also learn how to conduct unit tests on each part of your application.Learn how to use controllers for moving data to and from viewsUnderstand when to use AngularJS services instead of controllersCommunicate with the server to store, fetch, and update data asynchronouslyKnow when to use AngularJS filters for converting data and values to different formatsImplement single-page applications, using ngRoute to select views and navigationDive into basic and advanced directives for creating reusable componentsWrite an end-to-end test on a live version of your entire applicationUse best practices, guidelines, and tools throughout the development cycle

    For more than a year, Washington Post reporter Robert O'Harrow has explored the threats proliferating in our digital universe. This eBook is a compilation of that reporting. With chapters built around real people, including hackers, security researchers and corporate executives, this book will help regular people, lawmakers and businesses better understand the mind-bending challenge of keeping the internet safe from hackers and security breaches -- and all out war.

    With the 97 short and extremely useful tips for programmers in this book, you'll expand your skills by adopting new approaches to old problems, learning appropriate best practices, and honing your craft through sound advice.With contributions from some of the most experienced and respected practitioners in the industry--including Michael Feathers, Pete Goodliffe, Diomidis Spinellis, Cay Horstmann, Verity Stob, and many more--this book contains practical knowledge and principles that you can apply to all kinds of projects.A few of the 97 things you should know:"Code in the Language of the Domain" by Dan North"Write Tests for People" by Gerard Meszaros"Convenience Is Not an -ility" by Gregor Hohpe"Know Your IDE" by Heinz Kabutz"A Message to the Future" by Linda Rising"The Boy Scout Rule" by Robert C. Martin (Uncle Bob)"Beware the Share" by Udi Dahan

    In this transcript of the audio series, Gene Kim and John Willis present a nine-part discussion that includes an oral history of the DevOps movement, as well as discussions around pivotal figures and philosophies that DevOps draws upon, from Goldratt to Deming; from Lean to Safety Culture to Learning Organizations.The book is a great way for listeners to take an even deeper dive into topics relevant to DevOps and leading technology organizations.

    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.

    The time you spend context switching between your editor and your consoles eats away at your productivity. Take control of your environment with tmux, a terminal multiplexer that you can tailor to your workflow. With this updated second edition for tmux 2.3, you'll customize, script, and leverage tmux's unique abilities to craft a productive terminal environment that lets you keep your fingers on your keyboard's home row.You have a database console, web server, test runner, and text editor running at the same time, but switching between them and trying to find what you need takes up valuable time and breaks your concentration. By using tmux 2.3, you can improve your productivity and regain your focus. This book will show you how.This second edition includes many features requested by readers, including how to integrate plugins into your workflow, how to integrate tmux with Vim for seamless navigation - oh, and how to use tmux on Windows 10.Use tmux to manage multiple terminal sessions in a single window using only your keyboard. Manage and run programs side by side in panes, and create the perfect development environment with custom scripts so that when you're ready to work, your programs are waiting for you. Manipulate text with tmux's copy and paste buffers, so you can move text around freely between applications. Discover how easy it is to use tmux to collaborate remotely with others, and explore more advanced usage as you manage multiple tmux sessions, add custom scripts into the tmux status line, and integrate tmux with your system.Whether you're an application developer or a system administrator, you'll find many useful tricks and techniques to help you take control of your terminal.

    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.

    In 1999, "Refactoring" revolutionized design by introducing an effective process for improving code. With the highly anticipated " Refactoring to Patterns ," Joshua Kerievsky has changed our approach to design by forever uniting patterns with the evolutionary process of refactoring.This book introduces the theory and practice of pattern-directed refactorings: sequences of low-level refactorings that allow designers to safely move designs to, towards, or away from pattern implementations. Using code from real-world projects, Kerievsky documents the thinking and steps underlying over two dozen pattern-based design transformations. Along the way he offers insights into pattern differences and how to implement patterns in the simplest possible ways.Coverage includes: A catalog of twenty-seven pattern-directed refactorings, featuring real-world code examples Descriptions of twelve design smells that indicate the need for this book s refactorings General information and new insights about patterns and refactoringDetailed implementation mechanics: how low-level refactorings are combined to implement high-level patterns Multiple ways to implement the same pattern and when to use each Practical ways to get started even if you have little experience with patterns or refactoring"Refactoring to Patterns" reflects three years of refinement and the insights of more than sixty software engineering thought leaders in the global patterns, refactoring, and agile development communities. Whether you re focused on legacy or greenfield development, this book will make you a better software designer by helping you learn how to make important design changes safely and effectively. "

     Rails(TM) AntiPatterns identifies these widespread Rails code and design problems, explains why they're bad and why they happen--and shows exactly what to do instead.The book is organized into concise, modular chapters--each outlines a single common AntiPattern and offers detailed, cookbook-style code solutions that were previously difficult or impossible to find. Leading Rails developers Chad Pytel and Tammer Saleh also offer specific guidance for refactoring existing bad code or design to reflect sound object-oriented principles and established Rails best practices. With their help, developers, architects, and testers can dramatically improve new and existing applications, avoid future problems, and establish superior Rails coding standards throughout their organizations.This book will help you understand, avoid, and solve problems withModel layer code, from general object-oriented programming violations to complex SQL and excessive redundancy Domain modeling, including schema and database issues such as normalization and serialization View layer tools and conventions Controller-layer code, including RESTful code Service-related APIs, including timeouts, exceptions, backgrounding, and response codes Third-party code, including plug-ins and gems Testing, from test suites to test-driven development processes Scaling and deployment Database issues, including migrations and validations System design for "graceful degradation" in the real world

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    This means designing systems as small composable units, expressing constraints and interactions via the type system, and using composition to guide the construction of large systems in a way that maintains the original architectural vision.The book also serves as an introduction to the Cats library. We use abstractions from Cats, and we explain the structure of Cats so you can use it without fear in your own code base. The broad ideas are not specific to Cats, but Cats provides an excellent implementation that is beneficial to learn in its own right.

    As a result, many people are left with an incomplete understanding of this transformative new technology and its massive implications for the future. The goal of this book is not to plumb the depths of the mathematical wizardry used to code blockchain-based applications, but rather to serve as an introduction to the broader architecture and conceptual background behind blockchain technology. We will take a practical approach, examining how Blockchains are used in the real world. In this short, concise guide you will learn: A Brief History of Blockchain Technology Blockchain Basics: Managing Digital Transactions What is a Distributed Ledger? Blockchain Beyond Bitcoin Implications Of Blockchain: Big Data, Privacy & Personal Data Profiting from Blockchain Technologies Limitations & Challenges of Blockchain The Future of Blockchain For Centuries, people have relied on corrupt Centralized Institutions like banks and Governments to serve as intermediaries when it comes to storing and transacting financial assets. This is ALL About To Change... Make sure you take action and join the Financial Revolution by reading this book!

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    With the "You Don’t Know JS" book series, you’ll get a more complete understanding of JavaScript, including trickier parts of the language that many experienced JavaScript programmers simply avoid.The series’ first book, Up & Going, provides the necessary background for those of you with limited programming experience. By learning the basic building blocks of programming, as well as JavaScript’s core mechanisms, you’ll be prepared to dive into the other, more in-depth books in the series—and be well on your way toward true JavaScript.With this book you will: Learn the essential programming building blocks, including operators, types, variables, conditionals, loops, and functions Become familiar with JavaScript's core mechanisms such as values, function closures, this, and prototypes Get an overview of other books in the series—and learn why it’s important to understand all parts of JavaScript