- General
- Mason, Tom
- Whales
- Zoology
- Resnick, Mike
- Personal Finance
- General
- Inspirational
- Nonfiction
- Psychology of Sexuality
- Nevada
- Child, Maureen
- Martha Stewart
- Asian
- Communism
- Men
- Reform & Policy
- History
- General
- Biographies & Memoirs
- Transportation
- Fitzgerald, F. Scott
- Austria
- ( F )
- Hatch, Rob
- General
- Dairy Science
- Harlequin Love & Laughter
- Cats, Dogs & Animals
- Mazer, Norma Fox
- Some of our other sites:
- Books
- Clothing, Shoes and Accessories
- Baby Clothes and Accessories
- Cosmetics, Beauty Products and Fragrances
- Cellphones, Call Plans and Accessories
- Video Games
- DVDs
- Electronics, Gadgets and Computers
- Health and Personal Care
- Home and Garden
- Home DIY
- Jewelry
- Magazines and Newspapers
- Music Downloads
- Musical Instruments
- Office Equipment and Supplies
- Software and Games
- Sporting Goods
- Toys and Games
- Watches
- UK Books
- UK Video Games
- UK Home and Garden
- UK Electronics, Gadgets and Computers
- UK Baby Clothes and Accessories
- UK Software and Games
- UK Sporting Goods
- UK Toys and Games
Books : Computers & Internet : Computer Science : Artificial Intelligence : General
-
-
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect
-
-
Will the Geeks inherit the earth?
If computers become twice as fast and twice as capable every two years, how long is it before they’re as intelligent as humans? More intelligent? And then in two more years, twice as intelligent? How long before you won’t be able to tell if you are texting a person or an especially ingenious chatterbot program designed to simulate intelligent human conversation?
According to Richard Dooling in Rapture for the Geeks—maybe not that long. It took humans millions of years to develop opposable thumbs (which we now use to build computers), but computers go from megabytes to gigabytes in five years; from the invention of the PC to the Internet in less than fifteen. At the accelerating rate of technological development, AI should surpass IQ in the next seven to thirty-seven years (depending on who you ask). We are sluggish biological sorcerers, but we’ve managed to create whiz-bang machines that are evolving much faster than we are.
In this fascinating, entertaining, and illuminating book, Dooling looks at what some of the greatest minds have to say about our role in a future in which technology rapidly leaves us in the dust. As Dooling writes, comparing human evolution to technological evolution is “worse than apples and oranges: It’s appliances versus orangutans.” Is the era of Singularity, when machines outthink humans, almost upon us? Will we be enslaved by our supercomputer overlords, as many a sci-fi writer has wondered? Or will humans live lives of leisure with computers doing all the heavy lifting?
With antic wit, fearless prescience, and common sense, Dooling provocatively examines nothing less than what it means to be human in what he playfully calls the age of b.s. (before Singularity)—and what life will be like when we are no longer alone with Mother Nature at Darwin’s card table. Are computers thinking and feeling if they can mimic human speech and emotions? Does processing capability equal consciousness? What happens to our quaint beliefs about God when we’re all worshipping technology? What if the human compulsion to create ever more capable machines ultimately leads to our own extinction? Will human ingenuity and faith ultimately prevail over our technological obsessions? Dooling hopes so, and his cautionary glimpses into the future are the best medicine to restore our humanity.
-
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
-
-
-
Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. TuringMathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.
The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.
Interwoven into the narrative are the highlights of Turing’s own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.
-
Now, completely updated for LabVIEW 6i. Reflects the latest enhancements in National Instruments' LabVIEW 6i. Designed for non-experts. Softcover. CD-ROM included.
-
Loads of pictures and very frank discussion make this book a pleasure to read, and a real learning tool.
— Craig Maloney, Slashdot Contributor
The author gives lots of practical advice, some of which would be useful even to experienced tinkerers. It is very thorough.
— Edward Chin, The Canadian Linux Users' Exchange
Learning robotics by yourself isnt easy, but it helps when the encouragement comes from an expert whos spent years in the field. Not only does Author David Cook assist you in understanding the component parts of robot development, but he also presents valuable techniques that prepare you to achieve new discoveries on your own.
Cook begins with the anatomy of a homemade robot and gives you the best advice on how to proceed successfully. General sources for tools and parts are provided in a consolidated list, and specific parts are recommended throughout the book. Also, basic safety precautions and essential measuring and numbering systems are promoted throughout.
Specific tools and parts covered include digital multimeters, motors, wheels, resistors, LEDs, photoresistors, transistors, chips, gears, nut drivers, batteries, and more. Robot Building for Beginners is an inspiring book that provides an essential base of practical knowledge for anyone getting started in amateur robotics.
-
-
Richard K. Morgan has received widespread praise for his astounding twenty-fifth-century novels featuring Takeshi Kovacs, and has established a growing legion of fans. Mixing classic noir sensibilities with a searing futuristic vision of an age when death is nearly meaningless, Morgan returns to his saga of betrayal, mystery, and revenge, as Takeshi Kovacs, in one fatal moment, joins forces with a mysterious woman who may have the power to shatter Harlan’s World forever.
Once a gang member, then a marine, then a galaxy-hopping Envoy trained to wreak slaughter and suppression across the stars, a bleeding, wounded Kovacs was chilling out in a New Hokkaido bar when some so-called holy men descended on a slim beauty with tangled, hyperwired hair. An act of quixotic chivalry later and Kovacs was in deep: mixed up with a woman with two names, many powers, and one explosive history.
In a world where the real and virtual are one and the same and the dead can come back to life, the damsel in distress may be none other than the infamous Quellcrist Falconer, the vaporized symbol of a freedom now gone from Harlan’s World. Kovacs can deal with the madness of AI. He can do his part in a battle against biomachines gone wild, search for a three-centuries-old missing weapons system, and live with a blood feud with the yakuza, and even with the betrayal of people he once trusted. But when his relationship with “the” Falconer brings him an enemy specially designed to destroy him, he knows it’s time to be afraid.
After all, the guy sent to kill him is himself: but younger, stronger, and straight out of hell.
Wild, provocative, and riveting, Woken Furies is a full-bore science fiction spectacular of the highest order–from one of the most original and spellbinding storytellers at work today.
-
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
-
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.
Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.
This book is a suitable companion book for an introductory course on Bayesian methods. Also the book is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book.
-
This guide supports an innovative approach to fundamental programming concepts. The authors use program visualization to create an easy relationship between program construct and the animation action in a 3D world. Final release is in full color. For consistency with Java, C++, and other commonly used languages, "questions" are now "functions." Save and reload bugs have been fixed. Fonts can be scaled larger or smaller. High contrast mode is available for projection in the classroom. A much larger local gallery is now loaded with Alice; the CD with the book contains the complete gallery, so Internet access for downloading 3D models is no longer required. A useful how-to guide for programmers interested in learning Alice.
-
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface
-
This book describes in detail many of the AI techniques used in modern computer games, explicity shows how to implement these practical techniques within the framework of several game developers with a practical foundation to game AI.
-
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
-
Concepts of Database Management is the perfect short yet complete introduction to database concepts. The two featured case problems, Premiere Products and Henry Books, bring to life real-world database issues such as database design, data integrity, concurrent updates, and data security. This edition includes expanded coverage of SQL, entity-relationship (E-R) diagrams, normalization, and database design.
-
Learning is becoming an urgent topic. Nations worry about the learning of their citizens, companies about the learning of their workers, schools about the learning of their students. But it is not always easy to think about how to foster learning in innovative ways. This book presents a framework for doing that, with a social theory of learning that is ground-breaking yet accessible, with profound implications not only for research, but also for all those who have to foster learning as part of their responsibilites at work, at home, at school.