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Functional Web Development with Elixir, OTP, and Phoenix
Functional Web Development with Elixir, OTP, and Phoenix

Elixir and OTP provide exceptional tools to build rock-solid back-end applications that scale. In this book, you'll build a web application in a radically different way, with a back end that holds application state. You'll use persistent Phoenix Channel connections instead of HTTP's request-response, and create the full application in distinct, decoupled layers. In Part 1, start by building the business logic as a separate application, without Phoenix. Model the application domain with Elixir functions and simple data structures. By keeping state in memory instead of a database, you can reduce latency and simplify your code. In Part 2, add in the GenServer Behaviour to make managing in-memory state a breeze. Create a supervision tree to boost fault tolerance while separating error handling from business logic. Phoenix is a modern web framework you can layer on top of business logic while keeping the two completely decoupled. In Part 3, you'll do exactly that as you build a web in ...
Domain Modeling Made Functional
Domain Modeling Made Functional

Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming—all the techniques you need will be introduced and explained. Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation—ensuring that the code and design never get out of sync. Encode business rules in the design so that you have “compile-time unit tests,” and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and he ...
React for Real
React for Real

Dive right into React by defining components, the basic building blocks of a React application. Integrate modern JavaScript language features such as classes and arrow functions in your app. Analyze the relationships in your data to isolate state, and sync the data model with what your users see. Once you're familiar with how a React application works, organize your code base with modules. Configure a production build and deliver your app as efficiently as possible with Webpack. Master testing with React-specific advice and tools to catch the most bugs with the least amount of code. Learn the basics of the Redux library. Define actions and manage an immutable central state with reducers, then connect Redux to your React components to build even larger and more complex interfaces. Package your React code as a standalone widget so anyone can use it in their own applications. Reuse existing JavaScript code in your React components, and build a new React view on top of an existing data ...
Practical Programming, 3rd Edition
Practical Programming, 3rd Edition

No programming experience required! Incremental examples show you the steps and missteps that happen while developing programs, so you know what to expect when you tackle a problem on your own. Inspired by “How to Design Programs” (HtDP), discover a five-step recipe for designing functions, which helps you learn the concepts—and becomes an integral part of writing programs. In this detailed introduction to Python and to computer programming, find out exactly what happens when your programs are executed. Work with numbers, text, big data sets, and files using real-world examples. Create and use your own data types. Make your programs reliable, work with databases, download data from the web automatically, and build user interfaces. As you use the fundamental programming tools in this book, you'll see how to document and organize your code so that you and other programmers can more easily read and understand it. This new edition takes advantage of Python 3.6's new features, incl ...
Node.js 8 the Right Way
Node.js 8 the Right Way

JavaScript is the backbone of the modern web, powering nearly every web app's user interface. Node.js is JavaScript for the server. This greatly expanded second edition introduces new language features while dramatically increasing coverage of core topics. Each hands-on chapter offers progressively more challenging topics and techniques, broadening your skill set and enabling you to think in Node.js. Write asynchronous, non-blocking code using Node.js's style and patterns. Cluster and load balance services with Node.js core features and third-party tools. Harness the power of databases such as Elasticsearch and Redis. Work with many protocols, create RESTful web services, TCP socket clients and servers, and more. Test your code's functionality with Mocha, and manage its life cycle with npm. Discover how Node.js pairs a server-side event loop with a JavaScript runtime to produce screaming fast, non-blocking concurrency. Through a series of practical programming domains, use the lates ...
Python Testing with pytest
Python Testing with pytest

For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability—with no boilerplate code. With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, conn ...
Create Your Successful Agile Project
Create Your Successful Agile Project

You've tried to use an off-the-shelf approach to agile techniques, and it's not working. Instead of a standard method or framework, work from agile and lean principles to design your own agile approach in a way that works for you. Build collaborative, cross-functional teams. See how small batch sizes and frequent delivery create an environment of trust and transparency between the team, management, and customers. Learn about the interpersonal skills that help agile teams work together so well. In addition to seeing work and knowing what “done” means, you'll see examples of many possible team-based measurements. Look at tools you can use for status reporting, and how to use those measurements to help your managers understand what agile techniques buy them. Recognize the traps that prevent agile principles from working in too many organizations, and what to do about those traps. Use agile techniques for workgroups, and see what managers can do to create and nurture an agile cultur ...
Learning TensorFlow
Learning TensorFlow

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience - from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFl ...
Learning FPGAs
Learning FPGAs

Learn how to design digital circuits with FPGAs (field-programmable gate arrays), the devices that reconfigure themselves to become the very hardware circuits you set out to program. With this practical guide, author Justin Rajewski shows you hands-on how to create FPGA projects, whether you're a programmer, engineer, product designer, or maker. You'll quickly go from the basics to designing your own processor. Designing digital circuits used to be a long and costly endeavor that only big companies could pursue. FPGAs make the process much easier, and now they're affordable enough even for hobbyists. If you're familiar with electricity and basic electrical components, this book starts simply and progresses through increasingly complex projects. Set up your environment by installing Xilinx ISE and the author's Mojo IDE; Learn how hardware designs are broken into modules, comparable to functions in a software program; Create digital hardware designs and learn the basics on how they ...
Elegant SciPy
Elegant SciPy

Welcome to Scientific Python and its community. If you're a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You'll learn how to write elegant code that's clear, concise, and efficient at executing the task at hand. Throughout the book, you'll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you'll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation; Use quantile normalization to ensure that measurements fit a specific distribution; Represent separate regions in an image with a Region Adjacency Graph; Convert temporal or spatial data into frequency domain data with t ...
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