Building these systems is complicated and, because few formally established patterns are available for designing them, most of these systems end up looking very unique. This practical guide shows you how to use existing software design patterns for designing and building reliable distributed applications.Although patterns such as those developed more than 20 years ago by the Gang of Four were largely restricted to running on single machines, author Brendan Burns--a Partner Architect in Microsoft Azure--demonstrates how you can reuse several of them in modern distributed applications.Systems engineers and application developers will learn how these patterns provide a common language and framework for dramatically increasing the quality of your system.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    Learn You a Haskell for Great Good! introduces programmers familiar with imperative languages (such as C++, Java, or Python) to the unique aspects of functional programming. Packed with jokes, pop culture references, and the author's own hilarious artwork, Learn You a Haskell for Great Good! eases the learning curve of this complex language, and is a perfect starting point for any programmer looking to expand his or her horizons. The well-known web tutorial on which this book is based is widely regarded as the best way for beginners to learn Haskell, and receives over 30,000 unique visitors monthly.

    Rust's modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at compile time, making concurrency much easier to use.Written by two experienced systems programmers, this book explains how Rust manages to bridge the gap between performance and safety, and how you can take advantage of it. Topics include:How Rust represents values in memory (with diagrams)Complete explanations of ownership, moves, borrows, and lifetimesCargo, rustdoc, unit tests, and how to publish your code on crates.io, Rust's public package repositoryHigh-level features like generic code, closures, collections, and iterators that make Rust productive and flexibleConcurrency in Rust: threads, mutexes, channels, and atomics, all much safer to use than in C or C++Unsafe code, and how to preserve the integrity of ordinary code that uses itExtended examples illustrating how pieces of the language fit together

    However, successful applications have a habit of growing. Eventually the development team ends up in what is known as monolithic hell. All aspects of software development and deployment become painfully slow. The solution is to adopt the microservice architecture, which structures an application as a services, organized around business capabilities. This architecture accelerates software development and enables continuous delivery and deployment of complex software applications.Microservice Patterns teaches enterprise developers and architects how to build applications with the microservice architecture. Rather than simply advocating for the use the microservice architecture, this clearly-written guide takes a balanced, pragmatic approach. You'll discover that the microservice architecture is not a silver bullet and has both benefits and drawbacks. Along the way, you'll learn a pattern language that will enable you to solve the issues that arise when using the microservice architecture. This book also teaches you how to refactor a monolithic application to a microservice architecture.

    Generics and the greatly expanded collection libraries have tremendously increased the power of Java 5 and Java 6. But they have also confused many developers who haven't known how to take advantage of these new features.Java Generics and Collections covers everything from the most basic uses of generics to the strangest corner cases. It teaches you everything you need to know about the collections libraries, so you'll always know which collection is appropriate for any given task, and how to use it.Topics covered include:• Fundamentals of generics: type parameters and generic methods• Other new features: boxing and unboxing, foreach loops, varargs• Subtyping and wildcards• Evolution not revolution: generic libraries with legacy clients and generic clients with legacy libraries• Generics and reflection• Design patterns for generics• Sets, Queues, Lists, Maps, and their implementations• Concurrent programming and thread safety with collections• Performance implications of different collectionsGenerics and the new collection libraries they inspired take Java to a new level. If you want to take your software development practice to a new level, this book is essential reading.Philip Wadler is Professor of Theoretical Computer Science at the University of Edinburgh, where his research focuses on the design of programming languages. He is a co-designer of GJ, work that became the basis for generics in Sun's Java 5.0.Maurice Naftalin is Technical Director at Morningside Light Ltd., a software consultancy in the United Kingdom. He has most recently served as an architect and mentor at NSB Retail Systems plc, and as the leader of the client development team of a major UK government social service system."A brilliant exposition of generics. By far the best book on the topic, it provides a crystal clear tutorial that starts with the basics and ends leaving the reader with a deep understanding of both the use and design of generics." Gilad Bracha, Java Generics Lead, Sun Microsystems

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    While some fear is healthy (often viewed as a conscience that tells programmers to be careful!), the author believes that byproducts of fear include tentative, grumpy, and uncommunicative programmers who are unable to absorb constructive criticism. When programming teams buy into TDD, they immediately see positive results. They eliminate the fear involved in their jobs, and are better equipped to tackle the difficult challenges that face them. TDD eliminates tentative traits, it teaches programmers to communicate, and it encourages team members to seek out criticism However, even the author admits that grumpiness must be worked out individually! In short, the premise behind TDD is that code should be continually tested and refactored. Kent Beck teaches programmers by example, so they can painlessly and dramatically increase the quality of their work.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Understanding Computation explains theoretical computer science in a context you’ll recognize, helping you appreciate why these ideas matter and how they can inform your day-to-day programming.Rather than use mathematical notation or an unfamiliar academic programming language like Haskell or Lisp, this book uses Ruby in a reductionist manner to present formal semantics, automata theory, and functional programming with the lambda calculus. It’s ideal for programmers versed in modern languages, with little or no formal training in computer science.* Understand fundamental computing concepts, such as Turing completeness in languages* Discover how programs use dynamic semantics to communicate ideas to machines* Explore what a computer can do when reduced to its bare essentials* Learn how universal Turing machines led to today’s general-purpose computers* Perform complex calculations, using simple languages and cellular automata* Determine which programming language features are essential for computation* Examine how halting and self-referencing make some computing problems unsolvable* Analyze programs by using abstract interpretation and type systems

    An effective testing strategy will deliver new functionality more aggressively, accelerate user feedback, and improve quality. However, for many developers, creating effective automated tests is a unique and unfamiliar challenge. xUnit Test Patterns is the definitive guide to writing automated tests using xUnit, the most popular unit testing framework in use today. Agile coach and test automation expert Gerard Meszaros describes 68 proven patterns for making tests easier to write, understand, and maintain. He then shows you how to make them more robust and repeatable--and far more cost-effective. Loaded with information, this book feels like three books in one. The first part is a detailed tutorial on test automation that covers everything from test strategy to in-depth test coding. The second part, a catalog of 18 frequently encountered "test smells," provides trouble-shooting guidelines to help you determine the root cause of problems and the most applicable patterns. The third part contains detailed descriptions of each pattern, including refactoring instructions illustrated by extensive code samples in multiple programming languages. Topics covered includeWriting better tests--and writing them faster The four phases of automated tests: fixture setup, exercising the system under test, result verification, and fixture teardown Improving test coverage by isolating software from its environment using Test Stubs and Mock Objects Designing software for greater testability Using test "smells" (including code smells, behavior smells, and project smells) to spot problems and know when and how to eliminate them Refactoring tests for greater simplicity, robustness, and execution speed This book will benefit developers, managers, and testers working with any agile or conventional development process, whether doing test-driven development or writing the tests last. While the patterns and smells are especially applicable to all members of the xUnit family, they also apply to next-generation behavior-driven development frameworks such as RSpec and JBehave and to other kinds of test automation tools, including recorded test tools and data-driven test tools such as Fit and FitNesse.Visual Summary of the Pattern Language Foreword Preface Acknowledgments Introduction Refactoring a Test PART I: The Narratives Chapter 1 A Brief Tour Chapter 2 Test Smells Chapter 3 Goals of Test Automation Chapter 4 Philosophy of Test Automation Chapter 5 Principles of Test Automation Chapter 6 Test Automation Strategy Chapter 7 xUnit Basics Chapter 8 Transient Fixture Management Chapter 9 Persistent Fixture Management Chapter 10 Result Verification Chapter 11 Using Test Doubles Chapter 12 Organizing Our Tests Chapter 13 Testing with Databases Chapter 14 A Roadmap to Effective Test Automation PART II: The Test Smells Chapter 15 Code Smells Chapter 16 Behavior Smells Chapter 17 Project Smells PART III: The Patterns Chapter 18 Test Strategy Patterns Chapter 19 xUnit Basics Patterns Chapter 20 Fixture Setup Patterns Chapter 21 Result Verification Patterns Chapter 22 Fixture Teardown Patterns Chapter 23 Test Double Patterns Chapter 24 Test Organization Patterns Chapter 25 Database Patterns Chapter 26 Design-for-Testability Patterns Chapter 27 Value Patterns PART IV: Appendixes Appendix A Test Refactorings Appendix B xUnit Terminology Appendix C xUnit Family Members Appendix D Tools Appendix E Goals and Principles Appendix F Smells, Aliases, and Causes Appendix G Patterns, Aliases, and Variations Glossary References Index "