The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. 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. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    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.

    Google's newest driverless car had no steering wheel and no brakes. The message was clear: cars of the future will be born fully autonomous, with no human driver needed. In the coming decade, self-driving cars will hit the streets, rearranging established industries and reshaping cities, giving us new choices in where we live and how we work and play.In this book, Hod Lipson and Melba Kurman offer readers insight into the risks and benefits of driverless cars and a lucid and engaging explanation of the enabling technology. Recent advances in software and robotics are toppling long-standing technological barriers that for decades have confined self-driving cars to the realm of fantasy. A new kind of artificial intelligence software called deep learning gives cars rapid and accurate visual perception. Human drivers can relax and take their eyes off the road.When human drivers let intelligent software take the wheel, driverless cars will offer billions of people all over the world a safer, cleaner, and more convenient mode of transportation. Although the technology is nearly ready, car companies and policy makers may not be. The authors make a compelling case for why government, industry, and consumers need to work together to make the development of driverless cars our society's next "Apollo moment."

    Heart of the Machine explores the next giant step in the relationship between humans and technology: the ability of computers to recognize, respond to, and even replicate emotions. Computers have long been integral to our lives, and their advances continue at an exponential rate. Many believe that artificial intelligence equal or superior to human intelligence will happen in the not-too-distance future; some even think machine consciousness will follow. Futurist Richard Yonck argues that emotion, the first, most basic, and most natural form of communication, is at the heart of how we will soon work with and use computers.Instilling emotions into computers is the next leap in our centuries-old obsession with creating machines that replicate humans. But for every benefit this progress may bring to our lives, there is a possible pitfall. Emotion recognition could lead to advanced surveillance, and the same technology that can manipulate our feelings could become a method of mass control. And, as shown in movies like Her and Ex Machina, our society already holds a deep-seated anxiety about what might happen if machines could actually feel and break free from our control. Heart of the Machine is an exploration of the new and inevitable ways in which mankind and technology will interact.

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.

    What they don't realize—and what Daniel Hillis's short book brilliantly demonstrates—is that computers' seemingly complex operations can be broken down into a few simple parts that perform the same simple procedures over and over again.Computer wizard Hillis offers an easy-to-follow explanation of how data is processed that makes the operations of a computer seem as straightforward as those of a bicycle. Avoiding technobabble or discussions of advanced hardware, the lucid explanations and colorful anecdotes in The Pattern on the Stone go straight to the heart of what computers really do.Hillis proceeds from an outline of basic logic to clear descriptions of programming languages, algorithms, and memory. He then takes readers in simple steps up to the most exciting developments in computing today—quantum computing, parallel computing, neural networks, and self-organizing systems.Written clearly and succinctly by one of the world's leading computer scientists, The Pattern on the Stone is an indispensable guide to understanding the workings of that most ubiquitous and important of machines: the computer.

    By spinning 28 mathematical tales, Orlin shows us that calculus is simply another language to express the very things we humans grapple with every day -- love, risk, time, and most importantly, change. Divided into two parts, "Moments" and "Eternities," and drawing on everyone from Sherlock Holmes to Mark Twain to David Foster Wallace, Change is the Only Constant unearths connections between calculus, art, literature, and a beloved dog named Elvis. This is not just math for math's sake; it's math for the sake of becoming a wiser and more thoughtful human.

    Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

    of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel).Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Editorial reviews: "In his newest book, Architects of Intelligence, Martin Ford provides us with an invaluable opportunity to learn from some of the most prominent thought leaders about the emerging fields of science that are shaping our future." -Al Gore, Former Vice President of the US "AI is going to shape our future, and Architects of Intelligence offers a unique and fascinating collection of perspectives from the top researchers and entrepreneurs who are driving progress in the field." - Eric Schmidt, former Chairman and CEO, Google "The best way to understand the challenges and consequences of AGI is to see inside the minds of industry experts shaping the field. Architects of Intelligence gives you that power." -Sam Altman, President of Y Combinator and co-chairman of OpenAI "Architects of Intelligence gets you inside the minds of the people building the technology that is going to transform our world. This is a book that everyone should read." -Reid Hoffman, Co-founder of LinkedIn

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    It discusses cloud concepts and highlights various design patterns and best practices. This documentation is offered for free here as a Kindle book, or you can read it in PDF format at https://aws.amazon.com/whitepapers/.

    Readers will learn how to define the overall testing goals and strategies for the systems being tested.This hands-on, exercise-rich book provides experience with planning, scheduling, and tracking these tasks. You'll be able to describe and organize the necessary activities as well as learn to select, acquire, and assign adequate resources for testing tasks. Learn how to form, organize, and lead testing teams Master the organizing of communication among the members of the testing teams, and between the testing teams and all the other stakeholders. Additionally, you'll learn how to justify decisions and provide adequate reporting information where applicable.Target Audience: Software Testers, Programmers, System Designers, IT Managers

    This hands-on guide provides invaluable insight for creating successful test-driven development processes. With source code and examples featured in both C# and .NET, the book walks you through the TDD methodology and shows how it is applied to a real-world application. You'll witness the application built from scratch and details each step that is involved in the development, as well as any problems that were encountered and the solutions that were applied.Clarifies the motivation behind test-driven development (TDD), what it is, and how it works Reviews the various steps involved in developing an application and the testing that is involved prior to implementing the functionality Discusses unit testing and refactoring Professional Test-Driven Development with C# shows you how to create great TDD processes right away.

    But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization.Explore the basic concepts of color as a tool to highlight, distinguish, or represent a valueUnderstand the importance of redundant coding to ensure you provide key information in multiple waysUse the book's visualizations directory, a graphical guide to commonly used types of data visualizationsGet extensive examples of good and bad figuresLearn how to use figures in a document or report and how employ them effectively to tell a compelling story