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Vom PageRank zum heutigen Google
14,99 € *
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Studienarbeit aus dem Jahr 2016 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,3, Katholische Universität Eichstätt-Ingolstadt, Sprache: Deutsch, Abstract: Im ersten Kapitel erhält der Leser einen betriebswirtschaftlichen Einblick in das Unternehmen Google Inc. und dessen finanzielle Kennzahlen. Das Kapitel 3 greift prägende Entwicklungen von und für Google auf, die maßgeblich zum Erfolg beigetragen haben. Hier werden besonders die Funktionsweise einer Suchmaschine und der von Larry Page entwickelte PageRank-Algorithmus erklärt. In Kapitel 4 wird Googles Strategie erläutert. Abschließend werden in Kapitel 5 die Herausforderungen für den Weltkonzern diskutiert.

Anbieter: ciando eBooks
Stand: 14.11.2017
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Programming Collective Intelligence
32,99 € *
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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 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

Anbieter: buecher.de
Stand: 06.05.2018
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Nine Algorithms That Changed the Future
15,99 € *
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´´It´s been a long time since any book has given me the excitement I remember from reading Hawking and Feynman in my teens. This book does exactly that. It reminds me why I love computer science. MacCormick´s explanations are easy to understand yet they tell the real story of how these algorithms actually work. This is a book that deserves not just to be admired, but celebrated.´´--Andrew Fitzgibbon, creator of Emmy-winning camera software and consultant for the Xbox 360 Kinect´´This book is for those who have wondered, ´What actually goes on in my computer?´ MacCormick clearly explains some of the algorithms used by hundreds of millions of people daily. Not the simple algorithms like arithmetic and sorting, but more complex things such as how to determine the importance of web pages, if and when we are justified in trusting a computer-mediated conversation with another person, and the puzzling issue of what cannot be computed. I recommend it highly.´´--Chuck Thacker, winner of the 2010 Turing Award´´This is a delightful exploration, in layman´s terms, of nine beautiful algorithms that are essential to today´s computers. Using clever analogies, MacCormick gives readers a greater knowledge of both the technology they use every day and the intellectual underpinnings of computing. He combines a mathematician´s appreciation of powerful ideas and an educator´s skill at explaining them in an engaging way.´´--Sharon Perl, Google´´MacCormick picks nine algorithms for his version of ´genius awards,´ and they are good ones. The reader comes away with a new sense of what genius in computer science looks like. And MacCormick leaves room for a future genius, perhaps inspired by this book, to someday make it a top ten list.´´--William H. Press, coauthor of Numerical Recipes´´John MacCormick has taken many of the algorithms that we rely on every day and explained them in a way that you can understand even if you have a meager mathematical background. I particularly like how he explains public-key cryptography by analogy to mixing paint.´´--Thomas H. Cormen, Dartmouth College´´MacCormick does a great job of explaining sophisticated ideas in a simple way, and his analogies are wonderful. I particularly enjoyed the thoughtful and detailed historical asides.´´--Amy N. Langville, coauthor of Google´s PageRank and Beyond: The Science of Search Engine Rankings

Anbieter: buecher.de
Stand: 28.04.2018
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Erklärung des PageRank-Algorithmus von Google o...
12,99 € *
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Wissenschaftlicher Aufsatz aus dem Jahr 2016 im Fachbereich Informatik - Didaktik, Pädagogische Hochschule Kärnten Viktor Frankl Hochschule, Sprache: Deutsch, Abstract: Die Verwendung der Suchmaschine Google ist seit ihrer Veröffentlichung in den späten 1990er Jahren für einen Großteil der Internet-User Standard. Jede Art der Suche liefert die für den Benutzer relevanten Ergebnisse an den obersten Plätzen. Obwohl die mittlerweile zur Kulturtechnik (googeln) avancierte Verwendung dieser Suchmaschine von Kindheitsalter an angewendet wird, ist eine tiefergehende Auseinandersetzung im Unterricht nach wie vor nicht Standard. Spätestens seit dem großen Erfolg der Suchmaschine Google haben sich unzählige Artikel damit beschäftigt, wie der Suchalgorithmus von Google funktioniert. Um wissenschaftlich exakt zu bleiben, wird es dabei aber mathematisch sehr schnell kompliziert. Für die Beschreibung des PageRank-Algorithmus ist es notwendig, sich mit mehrstufigen Prozessen (Markov-Ketten), linearer Algebra (Übergangsmatrizen) und Analysis (Grenzwerte), auszukennen. In den dabei hergeleiteten Formeln wird dann (mathematisch exakt) das abstrakte mathematische Modell abgebildet. Genau diese Abstraktheit ist jedoch für Schüler der Sekundarstufe 1, und in der Regel auch für Schüler der Sekundarstufe 2, nicht fassbar. Es wird hier darum versucht, die Funktionsweise der Suchmaschine Google mit möglichst wenig Mathematik, dafür aber mit einem gewissen Maß an Intuition, zu erklären. Trotzdem wird versucht, ein möglichst korrektes Modell des PageRank-Algorithmus zu beschreiben.

Anbieter: ciando eBooks
Stand: 07.11.2017
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