They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.

    It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried."The first approach has been tried many times in both science fiction and reality. In this new novella, at over 30,000 words, his longest work to date, Ted Chiang offers a detailed imagining of how the second approach might work within the contemporary landscape of startup companies, massively-multiplayer online gaming, and open-source software. It's a story of two people and the artificial intelligences they helped create, following them for more than a decade as they deal with the upgrades and obsolescence that are inevitable in the world of software. At the same time, it's an examination of the difference between processing power and intelligence, and of what it means to have a real relationship with an artificial entity.

    The Text Was Designed To Familiarize Students With The Foundations And Principles Of Computer Science And To Strengthen The Students' Ability To Carry Out Formal And Rigorous Mathematical Arguments. In The New Fourth Edition, Author Peter Linz Has Offered A Straightforward, Uncomplicated Treatment Of Formal Languages And Automata And Avoids Excessive Mathematical Detail So That Students May Focus On And Understand The Underlying Principles. In An Effort To Further The Accessibility And Comprehension Of The Text, The Author Has Added New Illustrative Examples Throughout.

    It is not a mere reorganization of the data into a new kind of library catalogue, nor another specualtive philosophy about the nature of man and language, but rather a rigorus explication of our intuitions about our language in terms of an overt axiom system, the theorems derivable from it, explicit results which may be compared with new data and other intuitions, all based plainly on an overt theory of the internal structure of languages; and it may well provide an opportunity for the application of explicity measures of simplicity to decide preference of one form over another form of grammar.

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    Get the most out of BDD in Ruby with The RSpec Book, written by the lead developer of RSpec, David Chelimsky. You'll get started right away with RSpec 2 and Cucumber by developing a simple game, using Cucumber to express high-level requirements in language your customer understands, and RSpec to express more granular requirements that focus on the behavior of individual objects in the system. You'll learn how to use test doubles (mocks and stubs) to control the environment and focus the RSpec examples on one object at a time, and how to customize RSpec to "speak" in the language of your domain. You'll develop Rails 3 applications and use companion tools such as Webrat and Selenium to express requirements for web applications both in memory and in the browser. And you'll learn to specify Rails views, controllers, and models, each in complete isolation from the other. Whether you're developing applications, frameworks, or the libraries that power them, The RSpec Book will help you write better code, better tests, and deliver better software to happier users.

    This updated edition includes all the changes in Python 3, itself a significant shift from earlier versions of Python.The book begins with basic but useful programs that teach the core features of syntax, control flow, and data structures. It then moves to larger applications involving code management, object-oriented programming, web development, and converting code from earlier versions of Python.True to his audience of experienced developers, the author covers common programming language features concisely, while giving more detail to those features unique to Python.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

    The artificial intelligence system, created by DeepMind, had been fed nothing but the rules of the Royal Game when it beat the world’s strongest chess engine in a prolonged match. The selection of ten games published in December 2017 created a worldwide sensation: how was it possible to play in such a brilliant and risky style and not lose a single game against an opponent of superhuman strength?For Game Changer, Matthew Sadler and Natasha Regan investigated more than two thousand previously unpublished games by AlphaZero. They also had unparalleled access to its team of developers and were offered a unique look ‘under the bonnet’ to grasp the depth and breadth of AlphaZero’s search. Sadler and Regan reveal its thinking process and tell the story of the human motivation and the techniques that created AlphaZero.Game Changer also presents a collection of lucidly explained chess games of astonishing quality. Both professionals and club players will improve their game by studying AlphaZero’s stunning discoveries in every field that matters: opening preparation, piece mobility, initiative, attacking techniques, long-term sacrifices and much more.The story of AlphaZero has a wider impact. Game Changer offers intriguing insights into the opportunities and horizons of Artificial Intelligence. Not just in solving games, but in providing solutions for a wide variety of challenges in society.With a foreword by former World Chess Champion Garry Kasparov and an introduction by DeepMind CEO Demis Hassabis.Matthew Sadler (1974) is a Grandmaster who twice won the British Championship and was awarded an individual Gold Medal at the 1996 Olympiad. He has authored several highly acclaimed books on chess and has been writing the famous ‘Sadler on Books’ column for New In Chess magazine for many years. Natasha Regan is a Women’s International Master from England who achieved a degree in mathematics from Cambridge University. Matthew Sadler and Natasha Regan won the English Chess Federation 2016 Book of the Award for their book Chess for Life.

    . . But in fact, your data is your best defense against coercive marketing and intrusive government practices. Your data is nothing less than a superpower waiting to be harnessed.” —FROM THE INTRODUCTION In the past, the future was opaque—the territory of fortune-tellers, gurus, and dubious local TV weathermen. But thanks to recent advances in computing and the reams of data we create through smartphone and Internet use, prediction models for individual behavior grow smarter and more sophisticated by the day. Whom you should marry, whether you’ll commit a crime or fall victim to one, if you’ll contract a specific strain of flu—even your precise location at any given moment years into the future—are becoming easily accessible facts. The naked future is upon us, and the implications are staggering.Patrick Tucker draws on stories from health care to urban planning to online dating to reveal the shape of a future that’s ever more certain. In these pages you’ll meet scientists and inventors who can predict your behavior based on your friends’ Twitter updates. They are also hacking the New York City sewer system to predict environmental conditions, anticipating how much the weather a year from now will cost an individual farmer, figuring out the time of day you’re most likely to slip back into a bad habit, and guessing how well you’ll do on a test before you take it. You’ll learn how social networks like Facebook are using your data to turn you into an advertisement and why the winning formula for a blockbuster movie is more predictable than ever.The rise of big data and predictive analytics means that governments and corporations are becoming much more effective at accomplishing their goals and at much less cost. Tucker knows that’s not always a good thing. But he also shows how we’ve gained tremendous benefits that we have yet to fully realize.Thanks to the increased power of predictive science, we’ll be better able to stay healthy, invest our savings more wisely, learn faster and more efficiently, buy a house in the right neighborhood at the right time, avoid crime, thwart terrorists, and mitigate the consequences of natural disasters. What happens in a future that anticipates your every move? The surprising answer: we’ll live better as a result.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    But few of us realize just how many oversights, biases, and downright ethical nightmares are baked inside the tech products we use every day. It’s time we change that.In Technically Wrong, Sara Wachter-Boettcher demystifies the tech industry, leaving those of us on the other side of the screen better prepared to make informed choices about the services we use—and to demand more from the companies behind them.

    In this friendly, pragmatic book, cloud experts John Arundel and Justin Domingus show you what Kubernetes can do--and what you can do with it.You'll learn all about the Kubernetes ecosystem, and use battle-tested solutions to everyday problems. You'll build, step by step, an example cloud native application and its supporting infrastructure, along with a development environment and continuous deployment pipeline that you can use for your own applications.Understand containers and Kubernetes from first principles; no experience necessaryRun your own clusters or choose a managed Kubernetes service from Amazon, Google, and othersUse Kubernetes to manage resource usage and the container lifecycleOptimize clusters for cost, performance, resilience, capacity, and scalabilityLearn the best tools for developing, testing, and deploying your applicationsApply the latest industry practices for security, observability, and monitoringAdopt DevOps principles to help make your development teams lean, fast, and effective

    Pulitzer prize-winning New York Times science writer John Markoff argues that we must decide to design ourselves into our future, or risk being excluded from it altogether.In the past decade, Google introduced us to driverless cars; Apple debuted Siri, a personal assistant that we keep in our pockets; and an Internet of Things connected the smaller tasks of everyday life to the farthest reaches of the Web. Robots have become an integral part of society on the battlefield and the road; in business, education, and health care. Cheap sensors and powerful computers will ensure that in the coming years, these robots will act on their own. This new era offers the promise of immensely powerful machines, but it also reframes a question first raised more than half a century ago, when the intelligent machine was born. Will we control these systems, or will they control us?In Machines of Loving Grace, John Markoff offers a sweeping history of the complicated and evolving relationship between humans and computers. In recent years, the pace of technological change has accelerated dramatically, posing an ethical quandary. If humans delegate decisions to machines, who will be responsible for the consequences? As Markoff chronicles the history of automation, from the birth of the artificial intelligence and intelligence augmentation communities in the 1950s and 1960s, to the modern-day brain trusts at Google and Apple in Silicon Valley, and on to the expanding robotics economy around Boston, he traces the different ways developers have addressed this fundamental problem and urges them to carefully consider the consequences of their work. We are on the brink of the next stage of the computer revolution, Markoff argues, and robots will profoundly transform modern life. Yet it remains for us to determine whether this new world will be a utopia. Moreover, it is now incumbent upon the designers of these robots to draw a bright line between what is human and what is machine.After nearly forty years covering the tech industry, Markoff offers an unmatched perspective on the most drastic technology-driven societal shifts since the introduction of the Internet. Machines of Loving Grace draws on an extensive array of research and interviews to present an eye-opening history of one of the most pressing questions of our time, and urges us to remember that we still have the opportunity to design ourselves into the future—before it's too late.

    This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company, from analysts and management to the C-Suite and the board.Through interviews and examples from data scientists and analytics leaders in a variety of industries, author Carl Anderson explains the analytics value chain you need to adopt when building predictive business models—from data collection and analysis to the insights and leadership that drive concrete actions. You’ll learn what works and what doesn’t, and why creating a data-driven culture throughout your organization is essential. Start from the bottom up: learn how to collect the right data the right way Hire analysts with the right skills, and organize them into teams Examine statistical and visualization tools, and fact-based story-telling methods Collect and analyze data while respecting privacy and ethics Understand how analysts and their managers can help spur a data-driven culture Learn the importance of data leadership and C-level positions such as chief data officer and chief analytics officer