Numbers are often intimidating, confusing, and even deliberately deceptive—especially when they are really big. The media loves to report on millions, billions, and trillions, but frequently makes basic mistakes or presents such numbers in misleading ways. And misunderstanding numbers can have serious consequences, since they can deceive us in many of our most important decisions, including how to vote, what to buy, and whether to make a financial investment. In this short, accessible, enlightening, and entertaining book, leading computer scientist Brian Kernighan teaches anyone—even diehard math-phobes—how to demystify the numbers that assault us every day.
With examples drawn from a rich variety of sources, including journalism, advertising, and politics, Kernighan demonstrates how numbers can mislead and misrepresent. In chapters covering big numbers, units, dimensions, and more, he lays bare everything from deceptive graphs to speciously precise numbers. And he shows how anyone—using a few basic ideas and lots of shortcuts—can easily learn to recognize common mistakes, determine whether numbers are credible, and make their own sensible estimates when needed.
Giving you the simple tools you need to avoid being fooled by dubious numbers, Millions, Billions, Zillions is an essential survival guide for a world drowning in big—and often bad—data.
The book’s primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the “spin-net,” as a dynamic form of auto-associative memory.
This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.
Computers are everywhere. Some of them are highly visible, in laptops, tablets, cell phones, and smart watches. But most are invisible, like those in appliances, cars, medical equipment, transportation systems, power grids, and weapons. We never see the myriad computers that quietly collect, share, and sometimes leak vast amounts of personal data about us. Through computers, governments and companies increasingly monitor what we do. Social networks and advertisers know far more about us than we should be comfortable with, using information we freely give them. Criminals have all-too-easy access to our data. Do we truly understand the power of computers in our world?
Understanding the Digital World explains how computer hardware, software, networks, and systems work. Topics include how computers are built and how they compute; what programming is and why it is difficult; how the Internet and the web operate; and how all of these affect our security, privacy, property, and other important social, political, and economic issues. This book also touches on fundamental ideas from computer science and some of the inherent limitations of computers. It includes numerous color illustrations, notes on sources for further exploration, and a glossary to explain technical terms and buzzwords.
Understanding the Digital World is a must-read for all who want to know more about computers and communications. It explains, precisely and carefully, not only how they operate but also how they influence our daily lives, in terms anyone can understand, no matter what their experience and knowledge of technology.
The book features hundreds of interesting and practical examples of well-written Go code that cover the whole language, its most important packages, and a wide range of applications. Each chapter has exercises to test your understanding and explore extensions and alternatives. Source code is freely available for download from http://gopl.io/ and may be conveniently fetched, built, and installed using the go get command.