Offering indispensle lessons, practical advice, facts, anecdotes and inspiration, this book is a timeless creative bible for all those looking to succeed in life, business and creativity. These are key lessons derived from the incomparle life of 'Master Communicator' George Lois, the original Mad Man of Madison Avenue. Written and compiled by the man The Wall Street Journal called "prodigy, enfant terrible, founder of agencies, creator of legends," each step is borne from a passion to succeed and a disdain for the status quo.Organised into inspirational, bite-sized pointers, each page offers fresh insight into the sources of success, from identifying your heroes to identifying yourself. The ideas, images and illustrations presented in this book are fresh, witty and in-your-face. Whether it's communicating your point in nanosecond, creating an explosive portfolio or making your presence felt, no one is better placed than George Lois to teach you the process of creativity.Poignant, punchy and to-the-point, Damn Good Advice (For People With Talent!) is a must have for anyone on a quest for success.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    To Goethe, the theory was the result of mistaking an incidental result for an elemental principle. Far from pretending to a knowledge of physics, he insisted that such knowledge was an actual hindrance to understanding. He based his conclusions exclusively upon exhaustive personal observation of the phenomena of color.Of his own theory, Goethe was supremely confident: "From the philosopher, we believe we merit thanks for having traced the phenomena of colours to their first sources, to the circumstances under which they appear and are, and beyond which no further explanation respecting them is possible."Goethe's scientific conclusions have, of course, long since been thoroughly demolished, but the intelligent reader of today may enjoy this work on quite different grounds: for the beauty and sweep of his conjectures regarding the connection between color and philosophical ideas; for an insight into early nineteenth-century beliefs and modes of thought; and for the flavor of life in Europe just after the American and French Revolutions.The work may also be read as an accurate guide to the study of color phenomena. Goethe's conclusions have been repudiated, but no one quarrels with his reporting of the facts to be observed. With simple objects -- vessels, prisms, lenses, and the like -- the reader will be led through a demonstration course not only in subjectively produced colors, but also in the observable physical phenomena of color. By closely following Goethe's explanations of the color phenomena, the reader may become so divorced from the wavelength theory -- Goethe never even mentions it -- that he may begin to think about color theory relatively unhampered by prejudice, ancient or modern.

    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.

    Chased out of Germany by the Nazis, who banned and burned his books, Kahn emigrated to Palestine, then France, and finally the United States to continue his life's work. Though his achievements were numerous, the most notable was the development of creative visualizations to explain complex scientific ideas. Published on the 125th anniversary of Kahn's birth and destined to bring his work back into the spotlight, this monograph features more than 350 illustrations with extensive captions, three original texts by Fritz Kahn, a foreword by Steven Heller, and an essay about Kahn’s life and œuvre. Natural science buffs, graphics professionals, and anyone interested in visual expression of ideas will be fascinated by this tribute to Kahn’s greatest achievements.

    But as the field evolves, designers enter the field from many disciplines. Practitioners today have enough experience in UI design that they have been exposed to design rules, but it is essential that they understand the psychology behind the rules in order to effectively apply them. In "Designing with the Mind in Mind," Jeff Johnson, author of the best selling "GUI Bloopers," provides designers with just enough background in perceptual and cognitive psychology that UI design guidelines make intuitive sense rather than being just a list of rules to follow. * The first practical, all-in-one source for practitioners on user interface design rules and why, when and how to apply them.* Provides just enough background into the reasoning behind interface design rules that practitioners can make informed decisions in every project.* Gives practitioners the insight they need to make educated design decisions when confronted with tradeoffs, including competing design rules, time constrictions, or limited resources.

    The 16 project-based lessons show readers step-by-step the key techniques for working with InDesign CS6. Readers learn what they need to know to create engaging page layouts using InDesign CS6. This completely revised CS6 edition covers the new tools for adding PDF form fields, linking content, and creating alternative layouts for digital publishing. The companion CD includes all the lesson files that readers need to work along with the book. This thorough, self-paced guide to Adobe InDesign CS6 is ideal for beginning users who want to master the key features of this program. Readers who already have some experience with InDesign can improve their skills and learn InDesign's newest features. "The Classroom in a Book series is by far the best training material on the market. Everything you need to master the software is included: clear explanations of each lesson, step-by-step instructions, and the project files for the students." -Barbara Binder, Adobe Certified Instructor, Rocky Mountain Training Classroom in a Book(R), the best-selling series of hands-on software training workbooks, helps you learn the features of Adobe software quickly and easily. Classroom in a Book offers what no other book or training program does-an official training series from Adobe Systems Incorporated, developed with the support of Adobe product experts. All of Peachpit's eBooks contain the same content as the print edition. You will find a link in the last few pages of your eBook that directs you to the media files.Helpful tips:If you are able to search the book, search for "Where are the lesson files?"Go to the very last page of the book and scroll backwards.You will need a web-enabled device or computer in order to access the media files that accompany this ebook. Entering the URL supplied into a computer with web access will allow you to get to the files.Depending on your device, it is possible that your display settings will cut off part of the URL. To make sure this is not the case, try reducing your font size and turning your device to a landscape view. This should cause the full URL to appear.

    Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

    Yet information graphics is rarely taught in schools or is the focus of on-the-job training. Now, for the first time, Dona M. Wong, a student of the information graphics pioneer Edward Tufte, makes this material available for all of us. In this book, you will learn:to choose the best chart that fits your data;the most effective way to communicate with decision makers when you have five minutes of their time;how to chart currency fluctuations that affect global business;how to use color effectively;how to make a graphic “colorful” even if only black and white are available.The book is organized in a series of mini-workshops backed up with illustrated examples, so not only will you learn what works and what doesn’t but also you can see the dos and don’ts for yourself. This is an invaluable reference work for students and professional in all fields.

    Under the new authorial leadership of Alston Purvis, this authoritative book offers more than 450 new images, along with expansive coverage of such topics as Italian, Russian, and Dutch design. It reveals a saga of creative innovators, breakthrough technologies, and important design innovations.

    It’s about the language of video and how to think like a director, regardless of equipment (amateurs think about the camera, pros think about communication). It’s about the rules developed over a century of movie-making—which work just as well when shooting a two-year-old’s birthday party on your phone. Written by Steve Stockman, the director of the award-winning feature Two Weeks, plus TV shows, music videos, and hundreds of commercials, How to Shoot Video That Doesn’t Suck explains in 74 short, pithy, insightful chapters how to tell a story and entertain your audience. In other words, how to shoot video people will want to watch. Here’s how to think in shots—how to move-point-shoot-stop-repeat, instead of planting yourself in one spot and pressing “Record” for five minutes. Why never to shoot until you see the whites of your subject’s eyes. Why to “zoom” with your feet and not the lens. How to create intrigue on camera. The book covers the basics of video production: framing, lighting, sound (use an external mic), editing, special effects (turn them off!), and gives advice on shooting a variety of specific situations: sporting events, parties and family gatherings, graduations and performances. Plus, how to make instructional and promotional videos, how to make a music video, how to capture stunts, and much more. At the end of every chapter is a suggestion of how to immediately put what you’ve learned into practice, so the next time you’re shooting you’ll have begun to master the skill. Steve’s website (stevestockman.com) provides video examples to illustrate different production ideas, techniques, and situations, and his latest thoughts on all things video.

      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.

    Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.