It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Three dots to represent Dallas, Houston, and San Antonio. Three arrows to show direct flights. Problem solved, and the picture made it easy to sell Southwest Airlines to investors and customers. Used properly, a simple drawing on a humble napkin is more powerful than Excel or PowerPoint. It can help crystallize ideas, think outside the box, and communicate in a way that people simply “get”. In this book Dan Roam argues that everyone is born with a talent for visual thinking, even those who swear they can’t draw. Drawing on twenty years of visual problem solving combined with the recent discoveries of vision science, this book shows anyone how to clarify a problem or sell an idea by visually breaking it down using a simple set of visual thinking tools – tools that take advantage of everyone’s innate ability to look, see, imagine, and show. THE BACK OF THE NAPKIN proves that thinking with pictures can help anyone discover and develop new ideas, solve problems in unexpected ways, and dramatically improve their ability to share their insights. This book will help readers literally see the world in a new way.

     With a foreword by Larry Page, and contributions from Bono and Bill Gates. Measure What Matters is about using Objectives and Key Results (OKRs), a revolutionary approach to goal-setting, to make tough choices in business. In 1999, legendary venture capitalist John Doerr invested nearly $12 million in a startup that had amazing technology, entrepreneurial energy and sky-high ambitions, but no real business plan. Doerr introduced the founders to OKRs and with them at the foundation of their management, the startup grew from forty employees to more than 70,000 with a market cap exceeding $600 billion. The startup was Google. Since then Doerr has introduced OKRs to more than fifty companies, helping tech giants and charities exceed all expectations. In the OKR model objectives define what we seek to achieve and key results are how those top­ priority goals will be attained. OKRs focus effort, foster coordination and enhance workplace satisfaction. They surface an organization's most important work as everyone's goals from entry-level to CEO are transparent to the entire institution. In Measure What Matters, Doerr shares a broad range of first-person, behind-the-scenes case studies, with narrators including Bono and Bill Gates, to demonstrate the focus, agility, and explosive growth that OKRs have spurred at so many great organizations. This book will show you how to collect timely, relevant data to track progress - to measure what matters. It will help any organization or team aim high, move fast, and excel.

    And it’s still short, profusely illustrated…and best of all–fun to read.If you’ve read it before, you’ll rediscover what made Don’t Make Me Think so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.

    We need a brand new way to take it all in. 'Information is Beautiful' transforms the ideas surrounding and swamping us into graphs and maps that anyone can follow at a single glance.

    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

    At under 200 pages it's quick reading too. Makes a great airplane book.

    Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:* Retail sales and e-commerce* Inventory management* Procurement* Order management* Customer relationship management (CRM)* Human resources management* Accounting* Financial services* Telecommunications and utilities* Education* Transportation* Health care and insuranceBy the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:The Data Warehouse Toolkit, 2nd Edition (9780471200246)The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)The Data Warehouse ETL Toolkit (9780764567575)

    Through four years of groundbreaking research, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance—and what drives it—using rigorous statistical methods. This book presents both the findings and the science behind that research. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance.

    Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to fool rather than to inform.

    Think of the familiar map of the New York City subway system, or a diagram of the human brain. Successful visualizations are beautiful not only for their aesthetic design, but also for elegant layers of detail that efficiently generate insight and new understanding.This book examines the methods of two dozen visualization experts who approach their projects from a variety of perspectives -- as artists, designers, commentators, scientists, analysts, statisticians, and more. Together they demonstrate how visualization can help us make sense of the world.Explore the importance of storytelling with a simple visualization exerciseLearn how color conveys information that our brains recognize before we're fully aware of itDiscover how the books we buy and the people we associate with reveal clues to our deeper selvesRecognize a method to the madness of air travel with a visualization of civilian air trafficFind out how researchers investigate unknown phenomena, from initial sketches to published papers Contributors include:Nick Bilton, Michael E. Driscoll, Jonathan Feinberg, Danyel Fisher, Jessica Hagy, Gregor Hochmuth, Todd Holloway, Noah Iliinsky, Eddie Jabbour, Valdean Klump, Aaron Koblin, Robert Kosara, Valdis Krebs, JoAnn Kuchera-Morin et al., Andrew Odewahn, Adam Perer, Anders Persson, Maximilian Schich, Matthias Shapiro, Julie Steele, Moritz Stefaner, Jer Thorp, Fernanda Viegas, Martin Wattenberg, and Michael Young.

    Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process.

    She stared at a long list of logistics--from hiring to firing, from meeting to messaging, from planning to pitching--and faced a thousand questions and uncertainties. How was she supposed to spin teamwork into value? How could she be a good steward of her reports' careers? What was the secret to leading with confidence in new and unexpected situations?Now, having managed dozens of teams spanning tens to hundreds of people, Julie knows the most important lesson of all: great managers are made, not born. If you care enough to be reading this, then you care enough to be a great manager.The Making of a Manager is a modern field guide packed everyday examples and transformative insights, including:* How to tell a great manager from an average manager (illustrations included) * When you should look past an awkward interview and hire someone anyway * How to build trust with your reports through not being a boss * Where to look when you lose faith and lack the answersWhether you're new to the job, a veteran leader, or looking to be promoted, this is the handbook you need to be the kind of manager you wish you had.

    What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such issues for decades. And the solutions they've found have much to teach us.In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

    Cooper details many of these meta functions to explain his central thesis: programmers need to seriously re-evaluate the many user-hostile concepts deeply embedded within the software development process. Rather than provide users with a straightforward set of options, programmers often pile on the bells and whistles and ignore or de-prioritise lingering bugs. For the average user, increased functionality is a great burden, adding to the recurrent chorus that plays: "computers are hard, mysterious, unwieldy things." (An average user, Cooper asserts, who doesn't think that way or who has memorised all the esoteric commands and now lords it over others, has simply been desensitised by too many years of badly designed software.) Cooper's writing style is often overblown, with a pantheon of cutesy terminology (i.e. "dancing bearware") and insider back-patting. (When presenting software to Bill Gates, he reports that Gates replied: "How did you do that?" to which he writes: "I love stumping Bill!") More seriously, he is also unable to see beyond software development's importance--a sin he accuses programmers of throughout the book. Even with that in mind, the central questions Cooper asks are too important to ignore: Are we making users happier? Are we improving the process by which they get work done? Are we making their work hours more effective? Cooper looks to programmers, business managers and what he calls "interaction designers" to question current assumptions and mindsets. Plainly, he asserts that the goal of computer usage should be "not to make anyone feel stupid." Our distance from that goal reinforces the need to rethink entrenched priorities in software planning. -- Jennifer Buckendorff, Amazon.com