With this cookbook, you will find recipes for writing asynchronous, parallel, and reactive code in C# that enables your app or program to engage in more than one process at a time. Presented in O’Reilly’s popular problem-solution-discussion cookbook format, this guide provides ready-to-use code, along with an explanation of why and how the solution works.

    Cooper details many of these meta functions to explain his central thesis: programmers need to seriously re-evaluate the many user-hostile concepts deeply embedded within the software development process. Rather than provide users with a straightforward set of options, programmers often pile on the bells and whistles and ignore or de-prioritise lingering bugs. For the average user, increased functionality is a great burden, adding to the recurrent chorus that plays: "computers are hard, mysterious, unwieldy things." (An average user, Cooper asserts, who doesn't think that way or who has memorised all the esoteric commands and now lords it over others, has simply been desensitised by too many years of badly designed software.) Cooper's writing style is often overblown, with a pantheon of cutesy terminology (i.e. "dancing bearware") and insider back-patting. (When presenting software to Bill Gates, he reports that Gates replied: "How did you do that?" to which he writes: "I love stumping Bill!") More seriously, he is also unable to see beyond software development's importance--a sin he accuses programmers of throughout the book. Even with that in mind, the central questions Cooper asks are too important to ignore: Are we making users happier? Are we improving the process by which they get work done? Are we making their work hours more effective? Cooper looks to programmers, business managers and what he calls "interaction designers" to question current assumptions and mindsets. Plainly, he asserts that the goal of computer usage should be "not to make anyone feel stupid." Our distance from that goal reinforces the need to rethink entrenched priorities in software planning. -- Jennifer Buckendorff, Amazon.com

    The answers aren't easy -- just incredibly successful.

    Distinct from the details of implementation, algorithm, and data representation, an architecture holds the key to achieving system quality, is a reusable asset that can be applied to subsequent systems, and is crucial to a software organization's business strategy.Drawing on their own extensive experience, the authors cover the essential technical topics for designing, specifying, and validating a system. They also emphasize the importance of the business context in which large systems are designed. Their aim is to present software architecture in a real-world setting, reflecting both the opportunities and constraints that companies encounter. To that end, case studies that describe successful architectures illustrate key points of both technical and organizational discussions.Topics new to this edition include: Architecture design and analysis, including the Architecture Tradeoff Analysis Method (ATAM) Capturing quality requirements and achieving them through quality scenarios and tactics Using architecture reconstruction to recover undocumented architectures Documenting architectures using the Unified Modeling Language (UML) New case studies, including Web-based examples and a wireless Enterprise JavaBeans™ (EJB) system designed to support wearable computers The financial aspects of architectures, including use of the Cost Benefit Analysis Method (CBAM) to make decisions If you design, develop, or manage the building of large software systems (or plan to do so), or if you are interested in acquiring such systems for your corporation or government agency, use Software Architecture in Practice, Second Edition, to get up to speed on the current state of software architecture.

    At that career level, you’ll no longer be required to work towards the next promotion, and being promoted beyond it is exceptional rather than expected. At that point your career path will branch, and you have to decide between remaining at your current level, continuing down the path of technical excellence to become a Staff Engineer, or switching into engineering management. Of course, the specific titles vary by company, and you can replace “Senior Engineer” and “Staff Engineer” with whatever titles your company prefers. Over the past few years we’ve seen a flurry of books unlocking the engineering management career path, like Camille Fournier’s The Manager’s Path, Julie Zhuo’s The Making of a Manager, Lara Hogan’s Resilient Management and my own, An Elegant Puzzle. The management career isn’t an easy one, but increasingly there are maps available for navigating it. On the other hand, the transition into Staff Engineer, and its further evolutions like Principal and Distinguished Engineer, remains challenging and undocumented. What are the skills you need to develop to reach Staff Engineer? Are technical abilities alone sufficient to reach and succeed in that role? How do most folks reach this role? What is your manager’s role in helping you along the way? Will you enjoy being a Staff Engineer or you will toil for years to achieve a role that doesn’t suit you? "Staff Engineer: Leadership beyond the management track" is a pragmatic look at attaining and operating in these Staff-plus roles.

    However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

    This book is chock-full of material that will help you with your Django projects.We’ll introduce you to various tips, tricks, patterns, code snippets, and techniques that we’ve picked up over the years.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    His walk through the syntax and semantics of JavaScript is both charming and hugely insightful; reminders of gotchas complement realistic use cases, paced at a comfortable curve. You'll find when you finish the book that you've gained a strong and comprehensive sense of mastery." --Paul Irish, developer advocate, Google Chrome "This is not a book for those looking for shortcuts; rather it is hard-won experience distilled into a guided tour. It's one of the few books on JS that I'll recommend without hesitation." --Alex Russell, TC39 member, software engineer, Google In order to truly master JavaScript, you need to learn how to work effectively with the language's flexible, expressive features and how to avoid its pitfalls. No matter how long you've been writing JavaScript code, Effective JavaScript will help deepen your understanding of this powerful language, so you can build more predictable, reliable, and maintainable programs. Author David Herman, with his years of experience on Ecma's JavaScript standardization committee, illuminates the language's inner workings as never before--helping you take full advantage of JavaScript's expressiveness. Reflecting the latest versions of the JavaScript standard, the book offers well-proven techniques and best practices you'll rely on for years to come. Effective JavaScript is organized around 68 proven approaches for writing better JavaScript, backed by concrete examples. You'll learn how to choose the right programming style for each project, manage unanticipated problems, and work more successfully with every facet of JavaScript programming from data structures to concurrency. Key features include Better ways to use prototype-based object-oriented programming Subtleties and solutions for working with arrays and dictionary objects Precise and practical explanations of JavaScript's functions and variable scoping semantics Useful JavaScript programming patterns and idioms, such as options objects and method chaining In-depth guidance on using JavaScript's unique "run-to-completion" approach to concurrency

    Aiming to tell the dark secrets of computer arithmetic, this title is suitable for library developers, compiler writers, and lovers of elegant hacks.

    Phoenix creator Chris McCord, Elixir creator José Valim, and award-winning author Bruce Tate walk you through building an application that’s fast and reliable. At every step, you’ll learn from the Phoenix creators not just what to do, but why. Packed with insider insights, this definitive guide will be your constant companion in your journey from Phoenix novice to expert, as you build the next generation of web applications.

    Available here:readmeaway.com/download?i=1593279523The Linux Command Line, 2nd Edition: A Complete Introduction PDF by William ShottsRead The Linux Command Line, 2nd Edition: A Complete Introduction PDF from No Starch Press,William ShottsDownload William Shotts’s PDF E-book The Linux Command Line, 2nd Edition: A Complete Introduction

    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

    As a modern application developer you need to understand the emerging field of data management, both RDBMS and NoSQL. Seven Databases in Seven Weeks takes you on a tour of some of the hottest open source databases today. In the tradition of Bruce A. Tate's Seven Languages in Seven Weeks, this book goes beyond your basic tutorial to explore the essential concepts at the core each technology. Redis, Neo4J, CouchDB, MongoDB, HBase, Riak and Postgres. With each database, you'll tackle a real-world data problem that highlights the concepts and features that make it shine. You'll explore the five data models employed by these databases-relational, key/value, columnar, document and graph-and which kinds of problems are best suited to each. You'll learn how MongoDB and CouchDB are strikingly different, and discover the Dynamo heritage at the heart of Riak. Make your applications faster with Redis and more connected with Neo4J. Use MapReduce to solve Big Data problems. Build clusters of servers using scalable services like Amazon's Elastic Compute Cloud (EC2). Discover the CAP theorem and its implications for your distributed data. Understand the tradeoffs between consistency and availability, and when you can use them to your advantage. Use multiple databases in concert to create a platform that's more than the sum of its parts, or find one that meets all your needs at once.Seven Databases in Seven Weeks will take you on a deep dive into each of the databases, their strengths and weaknesses, and how to choose the ones that fit your needs.What You Need: To get the most of of this book you'll have to follow along, and that means you'll need a *nix shell (Mac OSX or Linux preferred, Windows users will need Cygwin), and Java 6 (or greater) and Ruby 1.8.7 (or greater). Each chapter will list the downloads required for that database.

    Concurrency and parallelism are the keys, and Seven Concurrency Models in Seven Weeks equips you for this new world. See how emerging technologies such as actors and functional programming address issues with traditional threads and locks development. Learn how to exploit the parallelism in your computer's GPU and leverage clusters of machines with MapReduce and Stream Processing. And do it all with the confidence that comes from using tools that help you write crystal clear, high-quality code. This book will show you how to exploit different parallel architectures to improve your code's performance, scalability, and resilience. Learn about the perils of traditional threads and locks programming and how to overcome them through careful design and by working with the standard library. See how actors enable software running on geographically distributed computers to collaborate, handle failure, and create systems that stay up 24/7/365. Understand why shared mutable state is the enemy of robust concurrent code, and see how functional programming together with technologies such as Software Transactional Memory (STM) and automatic parallelism help you tame it. You'll learn about the untapped potential within every GPU and how GPGPU software can unleash it. You'll see how to use MapReduce to harness massive clusters to solve previously intractible problems, and how, in concert with Stream Processing, big data can be tamed. With an understanding of the strengths and weaknesses of each of the different models and hardware architectures, you'll be empowered to tackle any problem with confidence.What You Need: The example code can be compiled and executed on *nix, OS X, or Windows. Instructions on how to download the supporting build systems are given in each chapter.