Programming Collective Intelligence

Building Smart Web 2.0 Applications

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Author: Toby Segaran

Publisher: "O'Reilly Media, Inc."

ISBN: 0596550685

Category: Computers

Page: 362

View: 5979

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 a dataset 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

Programming Collective Intelligence

Building Smart Web 2.0 Applications

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Author: Toby Segaran

Publisher: "O'Reilly Media, Inc."

ISBN: 0596517602

Category: Computers

Page: 360

View: 3940

Provides information on building Web 2.0 applications that have the capability to mine data created by Internet applications.

Machine Learning for Hackers

Case Studies and Algorithms to Get You Started

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Author: Drew Conway,John Myles White

Publisher: "O'Reilly Media, Inc."

ISBN: 1449330533

Category: Computers

Page: 324

View: 9499

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

Collective Intelligence in Action

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Author: Satnam Alag

Publisher: Manning Publications

ISBN: 9781933988313

Category: Computers

Page: 397

View: 3144

Provides information on using a Java-based CI toolkit to mine information to build more effective Web sites.

Web Squared: Web 2.0 Five Years On

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Author: Tim O'Reilly,John Battelle

Publisher: "O'Reilly Media, Inc."

ISBN: 144936571X

Category: Computers

Page: 15

View: 2709

Ever since we first introduced the term Web 2.0, people have been asking, What ™s next? Assuming that Web 2.0 was meant to be a kind of software version number (rather than a statement about the second coming of the Web after the dotcom bust), we ™re constantly asked about Web 3.0. Is it the semantic web? The sentient web? Is it the social web? The mobile web? Is it some form of virtual reality? It is all of those, and more. The Web is no longer a collection of static pages of HTML that describe something in the world. Increasingly, the Web is the world "everything and everyone in the world casts an information shadow,an aura of data which, when captured and processed intelligently, offers extraordinary opportunity and mindbending implications. Web Squared is our way of exploring this phenomenon and giving it a name.

Beautiful Data

The Stories Behind Elegant Data Solutions

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Author: Toby Segaran,Jeff Hammerbacher

Publisher: "O'Reilly Media, Inc."

ISBN: 144937929X

Category: Computers

Page: 386

View: 2624

In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

What is Web 2.0

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Author: Tim O'Reilly

Publisher: "O'Reilly Media, Inc."

ISBN: 1449391079

Category: Computers

Page: 12

View: 7525

The concept of "Web 2.0" began with a conference brainstorming session between O'Reilly and MediaLive International. Dale Dougherty, web pioneer and O'Reilly VP, noted that far from having "crashed", the web was more important than ever, with exciting new applications and sites popping up with surprising regularity. What's more, the companies that had survived the collapse seemed to have some things in common. Could it be that the dot-com collapse marked some kind of turning point for the web, such that a call to action such as "Web 2.0" might make sense? We agreed that it did, and so the Web 2.0 Conference was born. In the year and a half since, the term "Web 2.0" has clearly taken hold, with more than 9.5 million citations in Google. But there's still a huge amount of disagreement about just what Web 2.0 means, with some people decrying it as a meaningless marketing buzzword, and others accepting it as the new conventional wisdom. This article is an attempt to clarify just what we mean by Web 2.0.

Data Analysis with Open Source Tools

A Hands-On Guide for Programmers and Data Scientists

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Author: Philipp K. Janert

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449396657

Category: Computers

Page: 540

View: 3646

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. 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 variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

Mining the Social Web

Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

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Author: Matthew A. Russell,Matthew Russell

Publisher: "O'Reilly Media, Inc."

ISBN: 1449388345

Category: Computers

Page: 332

View: 5919

Provides information on data analysis from a vareity of social networking sites, including Facebook, Twitter, and LinkedIn.

Think Stats

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Author: Allen B. Downey

Publisher: "O'Reilly Media, Inc."

ISBN: 1491907371

Category: Computers

Page: 226

View: 4119

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

Data Mining: Practical Machine Learning Tools and Techniques

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Author: Ian H. Witten,Eibe Frank,Mark A. Hall

Publisher: Elsevier

ISBN: 0080890369

Category: Computers

Page: 664

View: 2645

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Artificial Intelligence for Humans, Volume 3

Deep Learning and Neural Networks

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Author: Jeff Heaton

Publisher: Createspace Independent Publishing Platform

ISBN: 9781505714340

Category:

Page: 374

View: 474

Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

Programming the Semantic Web

Build Flexible Applications with Graph Data

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Author: Toby Segaran,Colin Evans,Jamie Taylor

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449379179

Category: Computers

Page: 302

View: 3681

With this book, the promise of the Semantic Web -- in which machines can find, share, and combine data on the Web -- is not just a technical possibility, but a practical reality Programming the Semantic Web demonstrates several ways to implement semantic web applications, using current and emerging standards and technologies. You'll learn how to incorporate existing data sources into semantically aware applications and publish rich semantic data. Each chapter walks you through a single piece of semantic technology and explains how you can use it to solve real problems. Whether you're writing a simple mashup or maintaining a high-performance enterprise solution,Programming the Semantic Web provides a standard, flexible approach for integrating and future-proofing systems and data. This book will help you: Learn how the Semantic Web allows new and unexpected uses of data to emerge Understand how semantic technologies promote data portability with a simple, abstract model for knowledge representation Become familiar with semantic standards, such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL) Make use of semantic programming techniques to both enrich and simplify current web applications

Algorithms of the Intelligent Web

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Author: Douglas G McIlwraith,Haralambos Marmanis,Dmitry Babenko

Publisher: Manning Publications

ISBN: 9781617292583

Category: Computers

Page: 240

View: 5433

Summary Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction. About the Book Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you'll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python's scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You'll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning. What's Inside Introduction to machine learning Extracting structure from data Deep learning and neural networks How recommendation engines work About the Reader Knowledge of Python is assumed. About the Authors Douglas McIlwraith is a machine learning expert and data science practitioner in the field of online advertising. Dr. Haralambos Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions. Dmitry Babenko designs applications for banking, insurance, and supply-chain management. Foreword by Yike Guo. Table of Contents Building applications for the intelligent web Extracting structure from data: clustering and transforming your data Recommending relevant content Classification: placing things where they belong Case study: click prediction for online advertising Deep learning and neural networks Making the right choice The future of the intelligent web Appendix - Capturing data on the web

Mahout in Action

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Author: Sean Owen,Robin Anil,Ted Dunning

Publisher: Manning Publications

ISBN: 9781935182689

Category: Computers

Page: 387

View: 7027

Presents information on machine learning through the use of Apache Mahout, covering such topics as using group data to make individual recommendations, finding logical clusters, and filtering classifications.

Handbook of Computational Social Choice

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Author: Felix Brandt,Vincent Conitzer,Ulle Endriss,Jérôme Lang,Ariel D. Procaccia

Publisher: Cambridge University Press

ISBN: 1316489752

Category: Computers

Page: N.A

View: 807

The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.

Building Machine Learning Systems with Python

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Author: Willi Richert

Publisher: Packt Publishing Ltd

ISBN: 1782161414

Category: Computers

Page: 290

View: 9783

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.

REST API Design Rulebook

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Author: Mark Masse

Publisher: "O'Reilly Media, Inc."

ISBN: 1449310508

Category: Computers

Page: 94

View: 8093

The basic rules of REST APIs - "many nouns, few verbs, stick with HTTP" - seem easy, but that simplicity and power require discipline to work smoothly. This brief guide provides next steps for implementing complex projects on simple and extensible foundations.

Big Mind

How Collective Intelligence Can Change Our World

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Author: Geoff Mulgan

Publisher: N.A

ISBN: 9780691170794

Category: Business & Economics

Page: 280

View: 4308

A new field of collective intelligence has emerged in the last few years, prompted by a wave of digital technologies that make it possible for organizations and societies to think at large scale. This "bigger mind"--human and machine capabilities working together--has the potential to solve the great challenges of our time. So why do smart technologies not automatically lead to smart results? Gathering insights from diverse fields, including philosophy, computer science, and biology, Big Mind reveals how collective intelligence can guide corporations, governments, universities, and societies to make the most of human brains and digital technologies. Geoff Mulgan explores how collective intelligence has to be consciously organized and orchestrated in order to harness its powers. He looks at recent experiments mobilizing millions of people to solve problems, and at groundbreaking technology like Google Maps and Dove satellites. He also considers why organizations full of smart people and machines can make foolish mistakes--from investment banks losing billions to intelligence agencies misjudging geopolitical events--and shows how to avoid them. Highlighting differences between environments that stimulate intelligence and those that blunt it, Mulgan shows how human and machine intelligence could solve challenges in business, climate change, democracy, and public health. But for that to happen we'll need radically new professions, institutions, and ways of thinking. Informed by the latest work on data, web platforms, and artificial intelligence, Big Mind shows how collective intelligence could help us survive and thrive.