Not in an editor, IDE, or design tool. You're well educated on how to work with software and hardware, but what about wetware--our own brains? Learning new skills and new technology is critical to your career, and it's all in your head. In this book by Andy Hunt, you'll learn how our brains are wired, and how to take advantage of your brain's architecture. You'll learn new tricks and tips to learn more, faster, and retain more of what you learn. You need a pragmatic approach to thinking and learning. You need to Refactor Your Wetware. Programmers have to learn constantly; not just the stereotypical new technologies, but also the problem domain of the application, the whims of the user community, the quirks of your teammates, the shifting sands of the industry, and the evolving characteristics of the project itself as it is built. We'll journey together through bits of cognitive and neuroscience, learning and behavioral theory. You'll see some surprising aspects of how our brains work, and how you can take advantage of the system to improve your own learning and thinking skills.In this book you'll learn how to:Use the Dreyfus Model of Skill Acquisition to become more expertLeverage the architecture of the brain to strengthen different thinking modesAvoid common "known bugs" in your mindLearn more deliberately and more effectivelyManage knowledge more efficientlyPrinted in full color.

    As they helped grow Google from a young start-up to a global icon, they relearned everything they knew about management. How Google Works is the sum of those experiences distilled into a fun, easy-to-read primer on corporate culture, strategy, talent, decision-making, communication, innovation, and dealing with disruption.The authors explain how the confluence of three seismic changes - the internet, mobile, and cloud computing - has shifted the balance of power from companies to consumers. The companies that will thrive in this ever-changing landscape will be the ones that create superior products and attract a new breed of multifaceted employees whom the authors dub 'smart creatives'. The management maxims ('Consensus requires dissension', 'Exile knaves but fight for divas', 'Think 10X, not 10%') are illustrated with previously unreported anecdotes from Google's corporate history.'Back in 2010, Eric and I created an internal class for Google managers,' says Rosenberg. 'The class slides all read 'Google confidential' until an employee suggested we uphold the spirit of openness and share them with the world. This book codifies the recipe for our secret sauce: how Google innovates and how it empowers employees to succeed.'

    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

    Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?   In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters."   In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.

    In Empires of Light, historian Jill Jonnes portrays this extraordinary trio and their riveting and ruthless world of cutting-edge science, invention, intrigue, money, death, and hard-eyed Wall Street millionaires. At the heart of the story are Thomas Alva Edison, the nation’s most famous and folksy inventor, creator of the incandescent light bulb and mastermind of the world’s first direct current electrical light networks; the Serbian wizard of invention Nikola Tesla, elegant, highly eccentric, a dreamer who revolutionized the generation and delivery of electricity; and the charismatic George Westinghouse, Pittsburgh inventor and tough corporate entrepreneur, an industrial idealist who in the era of gaslight imagined a world powered by cheap and plentiful electricity and worked heart and soul to create it. Edison struggled to introduce his radical new direct current (DC) technology into the hurly-burly of New York City as Tesla and Westinghouse challenged his dominance with their alternating current (AC), thus setting the stage for one of the eeriest feuds in American corporate history, the War of the Electric Currents. The battlegrounds: Wall Street, the 1893 Chicago World’s Fair, Niagara Falls, and, finally, the death chamber - Jonnes takes us on the tense walk down a prison hallway and into the sunlit room where William Kemmler, convicted ax murderer, became the first man to die in the electric chair. Empires of Light is the gripping history of electricity, the “mysterious fluid,” and how the fateful collision of Edison, Tesla, and Westinghouse left the world utterly transformed.

    Now they’ve written a guide to help readers make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.

    (Marginal cost is the cost of producing additional units of a good or service, if fixed costs are not counted.) While economists have always welcomed a reduction in marginal cost, they never anticipated the possibility of a technological revolution that might bring marginal costs to near zero, making goods and services priceless, nearly free, and abundant, and no longer subject to market forces.Now, a formidable new technology infrastructure—the Internet of things (IoT)—is emerging with the potential of pushing large segments of economic life to near zero marginal cost in the years ahead. Rifkin describes how the Communication Internet is converging with a nascent Energy Internet and Logistics Internet to create a new technology platform that connects everything and everyone. Billions of sensors are being attached to natural resources, production lines, the electricity grid, logistics networks, recycling flows, and implanted in homes, offices, stores, vehicles, and even human beings, feeding Big Data into an IoT global neural network. Prosumers can connect to the network and use Big Data, analytics, and algorithms to accelerate efficiency, dramatically increase productivity, and lower the marginal cost of producing and sharing a wide range of products and services to near zero, just like they now do with information goods.The plummeting of marginal costs is spawning a hybrid economy—part capitalist market and part Collaborative Commons—with far reaching implications for society, according to Rifkin. Hundreds of millions of people are already transferring parts of their economic lives to the global Collaborative Commons. Prosumers are plugging into the fledgling IoT and making and sharing their own information, entertainment, green energy, and 3D-printed products at near zero marginal cost. They are also sharing cars, homes, clothes and other items via social media sites, rentals, redistribution clubs, and cooperatives at low or near zero marginal cost. Students are enrolling in free massive open online courses (MOOCs) that operate at near zero marginal cost. Social entrepreneurs are even bypassing the banking establishment and using crowdfunding to finance startup businesses as well as creating alternative currencies in the fledgling sharing economy. In this new world, social capital is as important as financial capital, access trumps ownership, sustainability supersedes consumerism, cooperation ousts competition, and "exchange value" in the capitalist marketplace is increasingly replaced by "sharable value" on the Collaborative Commons.Rifkin concludes that capitalism will remain with us, albeit in an increasingly streamlined role, primarily as an aggregator of network services and solutions, allowing it to flourish as a powerful niche player in the coming era. We are, however, says Rifkin, entering a world beyond markets where we are learning how to live together in an increasingly interdependent global Collaborative Commons.

    Thiel's sexuality had been known to close friends and family, but he didn't consider himself a public figure, and believed the information was private. This post would be the casus belli for a meticulously plotted conspiracy that would end nearly a decade later with a $140 million dollar judgment against Gawker, its bankruptcy and with Nick Denton, Gawker's CEO and founder, out of a job. Only later would the world learn that Gawker's demise was not incidental--it had been masterminded by Thiel.For years, Thiel had searched endlessly for a solution to what he'd come to call the "Gawker Problem." When an unmarked envelope delivered an illegally recorded sex tape of Hogan with his best friend's wife, Gawker had seen the chance for millions of pageviews and to say the things that others were afraid to say. Thiel saw their publication of the tape as the opportunity he was looking for. He would come to pit Hogan against Gawker in a multi-year proxy war through the Florida legal system, while Gawker remained confidently convinced they would prevail as they had over so many other lawsuit--until it was too late. The verdict would stun the world and so would Peter's ultimate unmasking as the man who had set it all in motion. Why had he done this? How had no one discovered it? What would this mean--for the First Amendment? For privacy? For culture?In Holiday's masterful telling of this nearly unbelievable conspiracy, informed by interviews with all the key players, this case transcends the narrative of how one billionaire took down a media empire or the current state of the free press. It's a study in power, strategy, and one of the most wildly ambitious--and successful--secret plots in recent memory.Some will cheer Gawker's destruction and others will lament it, but after reading these pages--and seeing the access the author was given--no one will deny that there is something ruthless and brilliant about Peter Thiel's shocking attempt to shake up the world.

    The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes.Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.

    As recounted by Ben Rich, the operation's brilliant boss for nearly two decades, the chronicle of Lockheed's legendary Skunk Works is a drama of cold war confrontations and Gulf War air combat, of extraordinary feats of engineering & achievement against fantastic odds. Here are up-close portraits of the maverick band of scientists & engineers who made the Skunk Works so renowned. Filled with telling personal anecdotes & high adventure, with narratives from the CIA & from Air Force pilots who flew the many classified, risky missions, this book is a portrait of the most spectacular aviation triumphs of the 20th century.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it.But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era.Having worked in the field of artificial intelligence for over 30 years, Smith reveals the mounting evidence that the mechanical actors in our lives do indeed have, or at least express, morals: they're just not the morals of the progressive modern society that we imagined we were moving towards. Instead, as we are just beginning to see – in the US elections and Brexit to name but a few – there are increasing incidences of machine bigotry, greed and the crass manipulation of our basest instincts.It is easy to assume that these are the result of programmer prejudices or the product of dark forces manipulating the masses through the network of the Internet. But what if there is something more fundamental and explicitly mechanical at play, something inherent within technology itself?This book demonstrates how non-scientific ideas have been encoded deep into our technological infrastructure. Offering a rigorous, fresh perspective on how technology has brought us to this place, Rage Inside the Machine challenges the long-held assumption that technology is an apolitical and amoral force. Shedding light on little-known historical stories and investigating the complex connections between scientific philosophy, institutional prejudice and new technology, this book offers a new, honest and more truly scientific vision of ourselves.

    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

    But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart.Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now.Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures