For all but the most trivial software systems, you must pay close attention to its architecture, the conceptual glue that holds every phase of a project together.

    Turing Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be "computable," creating the field of computability theory in the process, a foundation of present-day computer programming.The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.Interwoven into the narrative are the highlights of Turing's own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.

    From creating new data-driven products through to increasing operational efficiency, big data has the potential to makeyour organization both more competitive and more innovative.As this emerging field transitions from the bleeding edge to enterprise infrastructure, it's vital to understand not only the technologies involved, but the organizational and cultural demands of being data-driven.Written by O'Reilly Radar's experts on big data, this anthology describes:- The broad industry changes heralded by the big data era- What big data is, what it means to your business, and how to start solving data problems- The software that makes up the Hadoop big data stack, and the major enterprise vendors' Hadoop solutions- The landscape of NoSQL databases and their relative merits- How visualization plays an important part in data work

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    Combining tools, core principles, and plenty of humor, The Agile Samurai gives you the tools and the attitude to deliver something of value every week, and make rolling software into production a non-event. You’ll see how agile software delivery really works and how to help your team get agile fast, while having fun along the way.

    In 1999, "Refactoring" revolutionized design by introducing an effective process for improving code. With the highly anticipated " Refactoring to Patterns ," Joshua Kerievsky has changed our approach to design by forever uniting patterns with the evolutionary process of refactoring.This book introduces the theory and practice of pattern-directed refactorings: sequences of low-level refactorings that allow designers to safely move designs to, towards, or away from pattern implementations. Using code from real-world projects, Kerievsky documents the thinking and steps underlying over two dozen pattern-based design transformations. Along the way he offers insights into pattern differences and how to implement patterns in the simplest possible ways.Coverage includes: A catalog of twenty-seven pattern-directed refactorings, featuring real-world code examples Descriptions of twelve design smells that indicate the need for this book s refactorings General information and new insights about patterns and refactoringDetailed implementation mechanics: how low-level refactorings are combined to implement high-level patterns Multiple ways to implement the same pattern and when to use each Practical ways to get started even if you have little experience with patterns or refactoring"Refactoring to Patterns" reflects three years of refinement and the insights of more than sixty software engineering thought leaders in the global patterns, refactoring, and agile development communities. Whether you re focused on legacy or greenfield development, this book will make you a better software designer by helping you learn how to make important design changes safely and effectively. "

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    This book’s toolkit provides architects, product managers, technology managers, and executives with a shared language—in the form of repeatable, practical patterns and templates—to produce great technology strategies. Author Eben Hewitt developed 39 patterns over the course of a decade in his work as CTO, CIO, and chief architect for several global tech companies. With these proven tools, you can define, create, elaborate, refine, and communicate your architecture goals, plans, and approach in a way that executives can readily understand, approve, and execute. This book covers: Architecture and strategy: Adopt a strategic architectural mindset to make a meaningful material impact Creating your strategy: Define the components of your technology strategy using proven patterns Communicating the strategy: Convey your technology strategy in a compelling way to a variety of audiences Bringing it all together: Employ patterns individually or in clusters for specific problems; use the complete framework for a comprehensive strategy

    It includes many new standards, new technologies and new vendor-specific terms.

     With a foreword by Larry Page, and contributions from Bono and Bill Gates. Measure What Matters is about using Objectives and Key Results (OKRs), a revolutionary approach to goal-setting, to make tough choices in business. In 1999, legendary venture capitalist John Doerr invested nearly $12 million in a startup that had amazing technology, entrepreneurial energy and sky-high ambitions, but no real business plan. Doerr introduced the founders to OKRs and with them at the foundation of their management, the startup grew from forty employees to more than 70,000 with a market cap exceeding $600 billion. The startup was Google. Since then Doerr has introduced OKRs to more than fifty companies, helping tech giants and charities exceed all expectations. In the OKR model objectives define what we seek to achieve and key results are how those top­ priority goals will be attained. OKRs focus effort, foster coordination and enhance workplace satisfaction. They surface an organization's most important work as everyone's goals from entry-level to CEO are transparent to the entire institution. In Measure What Matters, Doerr shares a broad range of first-person, behind-the-scenes case studies, with narrators including Bono and Bill Gates, to demonstrate the focus, agility, and explosive growth that OKRs have spurred at so many great organizations. This book will show you how to collect timely, relevant data to track progress - to measure what matters. It will help any organization or team aim high, move fast, and excel.

    Accidental Empires is the trenchant, vastly readable history of that industry, focusing as much on the astoundingly odd personalities at its core—Steve Jobs, Bill Gates, Mitch Kapor, etc. and the hacker culture they spawned as it does on the remarkable technology they created. Cringely reveals the manias and foibles of these men (they are always men) with deadpan hilarity and cogently demonstrates how their neuroses have shaped the computer business. But Cringely gives us much more than high-tech voyeurism and insider gossip. From the birth of the transistor to the mid-life crisis of the computer industry, he spins a sweeping, uniquely American saga of creativity and ego that is at once uproarious, shocking and inspiring.

    SAFe addresses this primary goal, and offers these further benefits: reduced time-to-market, improved quality, stronger alignment across geographically distributed multi-disciplinary teams, and collaboration across teams to deliver meaningful value to customers with reduced cycle time." --Sundaresan Jagadeesan, SW CoE Program Director, Philips To succeed in today's adapt-or-die marketplace, businesses must be able to rapidly change the way they create and deliver value to their customers. Hundreds of the world's most successful companies-including Intel, Capital One, AstraZeneca, Cisco, and Philips-have turned to the Scaled Agile Framework(R) (SAFe(R)) to achieve agility at scale and maintain a competitive edge. SAFe(R) 4.5 Distilled: Applying the Scaled Agile Framework(R) for Lean Enterprises explains how adopting SAFe can quickly improve time to market and increase productivity, quality, and employee engagement. In this book, you will Understand the business case for SAFe: its benefits, the problems it solves, and how to apply it Get an overview of SAFe across all parts of the business: team, program, value stream, and portfolio Learn why SAFe works: the power of SAFe's Lean-Agile mindset, values, and principles Discover how systems thinking, Agile development, and Lean product development form the underlying basis for SAFe Learn how to become a Lean-Agile leader and effectively drive an enterprise-wide transformation Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships.If you're a newcomer to both search and distributed systems, you'll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you'll follow a problem-based approach to learn why, when, and how to use Elasticsearch features.Understand how Elasticsearch interprets data in your documentsIndex and query your data to take advantage of search concepts such as relevance and word proximityHandle human language through the effective use of analyzers and queriesSummarize and group data to show overall trends, with aggregations and analyticsUse geo-points and geo-shapes--Elasticsearch's approaches to geolocationModel your data to take advantage of Elasticsearch's horizontal scalabilityLearn how to configure and monitor your cluster in production

    Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named "PM" (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important "pitch."