Although dashboards are potentially powerful, this potential is rarely realized. The greatest display technology in the world won't solve this if you fail to use effective visual design. And if a dashboard fails to tell you precisely what you need to know in an instant, you'll never use it, even if it's filled with cute gauges, meters, and traffic lights. Don't let your investment in dashboard technology go to waste.This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Information Dashboard Design will explain how to:Avoid the thirteen mistakes common to dashboard design Provide viewers with the information they need quickly and clearly Apply what we now know about visual perception to the visual presentation of information Minimize distractions, cliches, and unnecessary embellishments that create confusion Organize business information to support meaning and usability Create an aesthetically pleasing viewing experience Maintain consistency of design to provide accurate interpretation Optimize the power of dashboard technology by pairing it with visual effectiveness Stephen Few has over 20 years of experience as an IT innovator, consultant, and educator. As Principal of the consultancy Perceptual Edge, Stephen focuses on data visualization for analyzing and communicating quantitative business information. He provides consulting and training services, speaks frequently at conferences, and teaches in the MBA program at the University of California in Berkeley. He is also the author of Show Me the Numbers: Designing Tables and Graphs to Enlighten. Visit his website at www.perceptualedge.com.

    As the words "at work" suggest, Peter Seibel focuses on how his interviewees tackle the day–to–day work of programming, while revealing much more, like how they became great programmers, how they recognize programming talent in others, and what kinds of problems they find most interesting. Hundreds of people have suggested names of programmers to interview on the Coders at Work web site: http://www.codersatwork.com. The complete list was 284 names. Having digested everyone’s feedback, we selected 16 folks who’ve been kind enough to agree to be interviewed:- Frances Allen: Pioneer in optimizing compilers, first woman to win the Turing Award (2006) and first female IBM fellow- Joe Armstrong: Inventor of Erlang- Joshua Bloch: Author of the Java collections framework, now at Google- Bernie Cosell: One of the main software guys behind the original ARPANET IMPs and a master debugger- Douglas Crockford: JSON founder, JavaScript architect at Yahoo!- L. Peter Deutsch: Author of Ghostscript, implementer of Smalltalk-80 at Xerox PARC and Lisp 1.5 on PDP-1- Brendan Eich: Inventor of JavaScript, CTO of the Mozilla Corporation - Brad Fitzpatrick: Writer of LiveJournal, OpenID, memcached, and Perlbal - Dan Ingalls: Smalltalk implementor and designer- Simon Peyton Jones: Coinventor of Haskell and lead designer of Glasgow Haskell Compiler- Donald Knuth: Author of The Art of Computer Programming and creator of TeX- Peter Norvig: Director of Research at Google and author of the standard text on AI- Guy Steele: Coinventor of Scheme and part of the Common Lisp Gang of Five, currently working on Fortress- Ken Thompson: Inventor of UNIX- Jamie Zawinski: Author of XEmacs and early Netscape/Mozilla hackerWhat you’ll learn:How the best programmers in the world do their jobWho is this book for?Programmers interested in the point of view of leaders in the field. Programmers looking for approaches that work for some of these outstanding programmers.

    As society stands on the cusp of unprecedented change, Jerry Kaplan unpacks the latest advances in robotics, machine learning, and perception powering systems that rival or exceed human capabilities. Driverless cars, robotic helpers, and intelligent agents that promote our interests have the potential to usher in a new age of affluence and leisure — but as Kaplan warns, the transition may be protracted and brutal unless we address the two great scourges of the modern developed world: volatile labor markets and income inequality. He proposes innovative, free-market adjustments to our economic system and social policies to avoid an extended period of social turmoil. His timely and accessible analysis of the promise and perils of artificial intelligence is a must-read for business leaders and policy makers on both sides of the aisle.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    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.

    Whether you're building the next killer app, investing in a startup, or simply curious about the technology, this practical book is essential reading.Bitcoin, the first successful decentralized digital currency, is still in its infancy and it's already spawned a multi-billion dollar global economy. This economy is open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides you with the knowledge you need (passion not included).This book includes:A broad introduction to bitcoin--ideal for non-technical users, investors, and business executivesAn explanation of the technical foundations of bitcoin and cryptographic currencies for developers, engineers, and software and systems architectsDetails of the bitcoin decentralized network, peer-to-peer architecture, transaction lifecycle, and security principlesOffshoots of the bitcoin and blockchain inventions, including alternative chains, currencies, and applicationsUser stories, analogies, examples, and code snippets illustrating key technical concepts

     Universal Methods of Design serves as an invaluable compendium of methods that can be easily referenced and used by cross-disciplinary teams in nearly any design project.   Methods and techniques are organized alphabetically for ongoing, quick reference. Each method is presented in a two-page format. The left-hand page contains a concise description of the method, accompanied by references for further reading. On the right-hand page, images and cases studies for each method are presented visually. The relevant phases for design application are highlighted as numbered icons along the right side of the page, from phases 1 (planning) through 5 (launch and monitor).Build more meaningful products with these methods and more: A/B Testing, Affinity Diagramming, Behavioral Mapping, Bodystorming, Contextual Design, Critical Incident Technique, Directed Storytelling, Flexible Modeling, Image Boards, Graffiti Walls, Heuristic Evaluation, Parallel Prototyping, Simulation Exercises, Touchstone Tours, and Weighted Matrix.  This essential guide:Dismantles the myth that user research methods are complicated, expensive, and time-consumingCreates a shared meaning for cross-disciplinary design teamsIllustrates methods with compelling visualizations and case studiesCharacterizes each method at a glanceIndicates when methods are best employed to help prioritize appropriate design research strategiesUniversal Methods of Design is an essential resource for designers of all levels and specializations.

    Distinguishing agents (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting. From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

    Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

    Hoffman aptly demonstrates the mysterious constructive powers of our eye-brain machines using lots of simple drawings and diagrams to illustrate basic rules of the visual road. Many of the examples are familiar optical illusions--perspective-confounding cubes, a few lines that add up to a more complex shape than seems right. Hoffman also takes a cue from Oliver Sacks, employing anecdotes about people with various specific visual malfunctions to both further his mechanical explanation of visual intelligence and drive home how important this little-understood aspect of cognition can be in our lives. An especially intriguing example involves a boy, blind from birth, who is surgically given the power to see. At first, he is completely unable to visually distinguish objects familiar by touch, such as the cat and the dog. Other poignant examples show clearly how image construction is normally linked to our emotional well-being and sense of place. Visual Intelligence is a fascinating, confounding look (as it were) at an aspect of human physiology and psychology that very few of us think about much at all. --Therese Littleton

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    Though it may not seem this way for those who have been in the field for most of their careers, in the overall scheme of professions, software builders are relative "newbies." In the short history of the software field, a lot of facts have been identified, and a lot of fallacies promulgated. Those facts and fallacies are what this book is about. There's a problem with those facts-and, as you might imagine, those fallacies. Many of these fundamentally important facts are learned by a software engineer, but over the short lifespan of the software field, all too many of them have been forgotten. While reading Facts and Fallacies of Software Engineering , you may experience moments of "Oh, yes, I had forgotten that," alongside some "Is that really true?" thoughts. The author of this book doesn't shy away from controversy. In fact, each of the facts and fallacies is accompanied by a discussion of whatever controversy envelops it. You may find yourself agreeing with a lot of the facts and fallacies, yet emotionally disturbed by a few of them! Whether you agree or disagree, you will learn why the author has been called "the premier curmudgeon of software practice." These facts and fallacies are fundamental to the software building field-forget or neglect them at your peril!

    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.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.