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

    The goal of the book is to share the techniques of the best companies. This book is aimed primarily at Product Managers working on technology-powered products. That includes the hundreds of "tech companies" like Google, Facebook, Amazon, Twitter and the like, as well as the thousands of companies moving to leverage technology (financial companies, media companies, retailers, manufacturers, nearly every industry). Inspired covers companies from early stage start-ups to large, established companies. The products might be consumer products or devices, business services for small businesses to enterprises, internal tools, and developer platforms.Inspired is secondarily aimed at the designers, engineers, user researchers and data scientists that work closely with the product managers on product teams at these same companies.

    Building on the successful top-down approach of previous editions, this fourth edition continues with an early emphasis on application-layer paradigms and application programming interfaces, encouraging a hands-on experience with protocols and networking concepts.

    Aging computer systems present complex technical challenges for organizations both large and small, and Kill It with Fire provides sound strategies for spearheading modernization efforts.Kill It with Fire examines aging computer systems, the evolution of technology over time, and how organizations can modernize, maintain, and future-proof their current systems.In playful and engaging prose, Marianne Bellotti uses real-world case studies to illustrate the technical challenges of modernizing complex legacy systems, as well as the organizational challenges of time-intensive maintenance efforts. The book explains how to evaluate existing architecture, create upgrade plans, and handle communication structures. Team exercises and historical analyses of complex computer systems make this a valuable resource for those in both older and newer companies, and will help readers restore or create systems built to evolve as time goes on.

    Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.

    Restructured into six parts, this new edition covers a wide spectrum of software processes from initial requirements solicitation through design and development.

    In the late 1990s, it provided an optimistic model for public

    To leverage the performance and power of multiprocessor programming, also known as multicore programming, programmers need to learn the new principles, algorithms, and tools.The book will be of immediate use to programmers working with the new architectures. For example, the next generation of computer game consoles will all be multiprocessor-based, and the game industry is currently struggling to understand how to address the programming challenges presented by these machines. This change in the industry is so fundamental that it is certain to require a significant response by universities, and courses on multicore programming will become a staple of computer science curriculums.This book includes fully-developed Java examples detailing data structures, synchronization techniques, transactional memory, and more.Students in multiprocessor and multicore programming courses and engineers working with multiprocessor and multicore systems will find this book quite useful.

    It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs.The book contains 33 different styles for writing the term frequency task. The styles are grouped into nine categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency, and interactivity. The author verbalizes the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style, next shows an example program, and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    In Enterprise Architecture as Strategy: Creating a Foundation for Business Execution, authors Jeanne W. Ross, Peter Weill, and David C. Robertson show you how.The key? Make tough decisions about which processes you must execute well, then implement the IT systems needed to digitize those processes. Citing numerous companies worldwide, the authors show how constructing the right enterprise architecture enhances profitability and time to market, improves strategy execution, and even lowers IT costs. Though clear, engaging explanation, they demonstrate how to define your operating model—your vision of how your firm will survive and grow—and implement it through your enterprise architecture. Their counterintuitive but vital message: when it comes to executing your strategy, your enterprise architecture may matter far more than your strategy itself.

    We'll use the world's most popular functional programming language: JavaScript. Some may feel this is a poor choice as it's against the grain of the current culture which, at the moment, feels predominately imperative. However, I believe it is the best way to learn FP for several reasons:You likely use it every day at work.This makes it possible to practice and apply your acquired knowledge each day on real world programs rather than pet projects on nights and weekends in an esoteric FP language.We don't have to learn everything up front to start writing programs.In a pure functional language, you cannot log a variable or read a DOM node without using monads. Here we can cheat a little as we learn to purify our codebase. It's also easier to get started in this language since it's mixed paradigm and you can fall back on your current practices while there are gaps in your knowledge.The language is fully capable of writing top notch functional code.We have all the features we need to mimic a language like Scala or Haskell with the help of a tiny library or two. Object-oriented programming currently dominates the industry, but it's clearly awkward in JavaScript. It's akin to camping off of a highway or tap dancing in galoshes. We have to bind all over the place lest this change out from under us, we don't have classes[^Yet], we have various work arounds for the quirky behavior when the new keyword is forgotten, private members are only available via closures. To a lot of us, FP feels more natural anyways.That said, typed functional languages will, without a doubt, be the best place to code in the style presented by this book. JavaScript will be our means of learning a paradigm, where you apply it is up to you. Luckily, the interfaces are mathematical and, as such, ubiquitous. You'll find yourself at home with swiftz, scalaz, haskell, purescript, and other mathematically inclined environments.