This groundbreaking new book sets out the principles and technical practices that enable rapid, incremental delivery of high quality, valuable new functionality to users. Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours-- sometimes even minutes-no matter what the size of a project or the complexity of its code base. Jez Humble and David Farley begin by presenting the foundations of a rapid, reliable, low-risk delivery process. Next, they introduce the "deployment pipeline," an automated process for managing all changes, from check-in to release. Finally, they discuss the "ecosystem" needed to support continuous delivery, from infrastructure, data and configuration management to governance. The authors introduce state-of-the-art techniques, including automated infrastructure management and data migration, and the use of virtualization. For each, they review key issues, identify best practices, and demonstrate how to mitigate risks. Coverage includes - Automating all facets of building, integrating, testing, and deploying software - Implementing deployment pipelines at team and organizational levels - Improving collaboration between developers, testers, and operations - Developing features incrementally on large and distributed teams - Implementing an effective configuration management strategy - Automating acceptance testing, from analysis to implementation - Testing capacity and other non-functional requirements - Implementing continuous deployment and zero-downtime releases - Managing infrastructure, data, components and dependencies - Navigating risk management, compliance, and auditing Whether you're a developer, systems administrator, tester, or manager, this book will help your organization move from idea to release faster than ever--so you can deliver value to your business rapidly and reliably.

    Create a tiny virtual environment, called a container, for your application that includes only its particular set of dependencies. The Docker engine accounts for, manages, and builds these containers through functionality provided by the host operating system. Software running inside containers share the Linux OS and other resources, such as libraries, making their footprints radically smaller, and the containerized applications are easy to install, manage, and remove. Developers can package their applications without worrying about environment-specific deployment concerns, and the operations team gets cleaner, more efficient systems across the board. Better still, Docker is free and open source.Docker in Action teaches readers how to create, deploy, and manage applications hosted in Docker containers. The book starts with a clear explanation of the Docker model of virtualization, comparing this approach to the traditional hypervisor model. Developers will learn how to package applications in containers, including specific techniques for testing and distributing applications via Docker Hub and other registries. Readers will learn how to take advantage of the Linux OS features that Docker uses to run programs securely, and how to manage shared resources. Using carefully-designed examples, the book teaches you how to orchestrate containers and applications from installation to removal. Along the way, you'll learn techniques for using Docker on systems ranging from your personal dev-and-test machine to full-scale cloud deployments.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    

    This hands-on guide introduces you to AngularJS, the open source JavaScript framework that uses Model–view–controller (MVC) architecture, data binding, client-side templates, and dependency injection to create a much-needed structure for building web apps.Guided by two engineers who worked on AngularJS at Google, you’ll walk through the framework’s key features, and then build a working AngularJS app—from layout to testing, compiling, and debugging. You’ll learn how AngularJS helps reduce the complexity of your web app.Dive deep into Angular’s building blocks and learn how they work togetherGain maximum flexibility by separating logic, data, and presentation responsibilities with MVCAssemble your full app in the browser, using client-side templatesUse AngularJS directives to extend HTML with declarative syntaxCommunicate with the server and implement simple caching with the $http serviceUse dependency injection to improve refactoring, testability, and multiple environment designGet code samples for common problems you face in most web apps

    If you're a reasonably proficient programmer who can think logically, you have all the background you'll need. Stepanov and Rose introduce the relevant abstract algebra and number theory with exceptional clarity. They carefully explain the problems mathematicians first needed to solve, and then show how these mathematical solutions translate to generic programming and the creation of more effective and elegant code. To demonstrate the crucial role these mathematical principles play in many modern applications, the authors show how to use these results and generalized algorithms to implement a real-world public-key cryptosystem. As you read this book, you'll master the thought processes necessary for effective programming and learn how to generalize narrowly conceived algorithms to widen their usefulness without losing efficiency. You'll also gain deep insight into the value of mathematics to programming--insight that will prove invaluable no matter what programming languages and paradigms you use. You will learn aboutHow to generalize a four thousand-year-old algorithm, demonstrating indispensable lessons about clarity and efficiencyAncient paradoxes, beautiful theorems, and the productive tension between continuous and discreteA simple algorithm for finding greatest common divisor (GCD) and modern abstractions that build on itPowerful mathematical approaches to abstractionHow abstract algebra provides the idea at the heart of generic programmingAxioms, proofs, theories, and models: using mathematical techniques to organize knowledge about your algorithms and data structuresSurprising subtleties of simple programming tasks and what you can learn from themHow practical implementations can exploit theoretical knowledge

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    This book builds on the contributions and strengths of seminal technologies like Spring, Hibernate, and TopLink.EJB 3 is the most important innovation introduced in Java EE 5.0. EJB 3 simplifies enterprise development, abandoning the complex EJB 2.x model in favor of a lightweight POJO framework. The new API represents a fresh perspective on EJB without sacrificing the mission of enabling business application developers to create robust, scalable, standards-based solutions.EJB 3 in Action is a fast-paced tutorial, geared toward helping you learn EJB 3 and the Java Persistence API quickly and easily. For newcomers to EJB, this book provides a solid foundation in EJB. For the developer moving to EJB 3 from EJB 2, this book addresses the changes both in the EJB API and in the way the developer should approach EJB and persistence.

    But building a popular API with a thriving developer ecosystem is also one of the most challenging. With this practical guide, developers, architects, and tech leads will learn how to navigate complex decisions for designing, scaling, marketing, and evolving interoperable APIs.Authors Brenda Jin, Saurabh Sahni, and Amir Shevat explain API design theory and provide hands-on exercises for building your web API and managing its operation in production. You'll also learn how to build and maintain a following of app developers. This book includes expert advice, worksheets, checklists, and case studies from companies including Slack, Stripe, Facebook, Microsoft, Cloudinary, Oracle, and GitHub.Get an overview of request-response and event-driven API design paradigmsLearn best practices for designing an API that meets the needs of your usersUse a template to create an API design processScale your web API to support a growing number of API calls and use casesRegularly adapt the API to reflect changes to your product or businessProvide developer resources that include API documentation, samples, and tools

    The problems are challenging, well-motivated, and accessible. They are representative of the questions asked at interviews at the most exciting companies.The book begins with a summary of patterns for data structure, algorithms, and problem solving that will help you solve the most challenging interview problems. This is followed by chapters on basic and advanced data structures, algorithm design, concurrency, system design, probability and discrete mathematics. Each chapter starts with a brief review of key concepts and results followed by a deep and wide set of questions.EPI concludes with a summary of the nontechnical aspects of interviewing, including common mistakes, strategies for a great interview, perspectives from across the table, negotiating the best offer, and much more."This book is the best compilation of programming related problems I have seen. It is a great resource for a diverse set of topics when preparing for technical interviews, as a quick refresher in a subject area or when you are just looking for a brain teaser to challenge yourself." Shashank Gupta / Scaligent, formerly Engineering Manager, Amazon.com, Senior Engineering Manager, Yahoo!, Manager of Software Development, Cisco Systems

    It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript.This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data.Learn HTML, CSS, JavaScript, and SVG basicsDynamically generate web page elements from your data—and choose visual encoding rules to style themCreate bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layoutsUse smooth, animated transitions to show changes in your dataIntroduce interactivity to help users explore data through different viewsCreate customized geographic maps with dataExplore hands-on with downloadable code and over 100 examples

    This book presents all the important test design techniques in a single place and in a consistent and easy-to-digest format. An immediately useful handbook for test engineers, developers, quality assurance professionals and requirements and systems analysts, it enables you to: choose the best test case design; find software defects in less time and with fewer resources; and develop optimal strategies that help reduce the likelihood of costly errors. It also assists you in estimating the effort, time and cost of good testing. included, helping you to fully understand the practical applications of these techniques. From well-established techniques such as equivalence classes, boundary value analysis, decision tables and state-transition diagrams, to new techniques like use case testing, pairwise testing and exploratory testing, the book is a usful resource for testing professionals seeking to improve their skills and a handy reference for college-level courses in software test design.

    Reverse engineering is not about reading assembly code, but actually understanding how different pieces/components in a system work. To reverse engineer a system is to understand how it is constructed and how it works. The book provides: Coverage of x86, x64, and ARM. In the past x86 was the most common architecture on the PC; however, times have changed and x64 is becoming the dominant architecture. It brings new complexity and constructs previously not present in x86. ARM ("Advanced RISC Machine) "is very common in embedded / consumer electronic devices; for example, most if not all cell phones run on ARM. All of apple's i-devices run on ARM. This book will be the first book to cover all three.Discussion of Windows kernel-mode code (rootkits/drivers). This topic has a steep learning curve so most practitioners stay away from this area because it is highly complex. However, this book will provide a concise treatment of this topic and explain how to analyze drivers step-by-step.The book uses real world examples from the public domain. The best way to learn is through a combination of concept discussions, examples, and exercises. This book uses real-world trojans / rootkits as examples congruent with real-life scenariosHands-on exercises. End-of-chapter exercises in the form of conceptual questions and hands-on analysis so so readers can solidify their understanding of the concepts and build confidence. The exercises are also meant to teach readers about topics not covered in the book.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    Instead of hard-coding dependencies, such as specifying a database driver, you inject a list of services that a component may need. The services are then connected by a third party. This technique enables you to better manage future changes and other complexity in your software.About this BookDependency Injection in .NET introduces DI and provides a practical guide for applying it in .NET applications. The book presents the core patterns in plain C#, so you'll fully understand how DI works. Then you'll learn to integrate DI with standard Microsoft technologies like ASP.NET MVC, and to use DI frameworks like StructureMap, Castle Windsor, and Unity. By the end of the book, you'll be comfortable applying this powerful technique in your everyday .NET development.This book is written for C# developers. No previous experience with DI or DI frameworks is required. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read.What's InsideMany C#-based examplesA catalog of DI patterns and anti-patternsUsing both Microsoft and open source DI frameworksTabel of ContentsPART 1 PUTTING DEPENDENCY INJECTION ON THE MAPA Dependency Injection tasting menuA comprehensive exampleDI ContainersPART 2 DI CATALOGDI patternsDI anti-patternsDI refactoringsPART 3 DIY DIObject CompositionObject LifetimeInterceptionPART 4 DI CONTAINERSCastle WindsorStructureMapSpring.NETAutofacUnityMEF