Algorithms of the Intelligent Web by Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko


Algorithms of the clever internet, moment Edition teaches an important ways to algorithmic internet information research, allowing you to create your individual desktop studying functions that crunch, munge, and wrangle info accumulated from clients, net purposes, sensors and web site logs.

Purchase of the print publication contains a loose publication in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Valuable insights are buried within the tracks internet clients depart as they navigate pages and purposes. you could discover them through the use of clever algorithms just like the ones that experience earned fb, Google, and Twitter a spot one of the giants of net facts development extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you the way to create laptop studying functions that crunch and wrangle info accumulated from clients, internet purposes, and site logs. during this completely revised variation, you are going to examine clever algorithms that extract actual worth from facts. Key laptop studying options are defined with code examples in Python's scikit-learn. This booklet publications you thru algorithms to seize, shop, and constitution facts streams coming from the net. you will discover suggestion engines and dive into type through statistical algorithms, neural networks, and deep learning.

What's Inside

  • Introduction to desktop learning
  • Extracting constitution from data
  • Deep studying and neural networks
  • How advice engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a computing device studying specialist and information technological know-how practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of desktop studying innovations for commercial suggestions. Dmitry Babenko designs purposes for banking, assurance, and supply-chain administration. Foreword by means of Yike Guo.

Table of Contents

  1. Building purposes for the clever web
  2. Extracting constitution from facts: clustering and remodeling your information
  3. Recommending suitable content
  4. Classification: putting issues the place they belong
  5. Case learn: click on prediction for on-line advertising
  6. Deep studying and neural networks
  7. Making definitely the right choice
  8. The way forward for the clever web
  9. Appendix - shooting information at the web

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Additional info for Algorithms of the Intelligent Web

Example text

James R. Flynn, What Is Intelligence? Beyond the Flynn Effect (Cambridge University Press, 2009). ” Sandeep Rajani, “Artificial Intelligence - Man or Machine,” International Journal of Information Technology and Knowledge Management 4, no. 1 (2011): 173–76. 13 Evaluating the performance of intelligent algorithms the relationship between the features and the target (or class). Consequently, we have a concrete framework for evaluation and may look to statistics for a formal treatment of performance.

You may have heard of the Gaussian distribution, sometimes known as the normal distribution, but what exactly is it? We’ll give you the mathematical definition shortly, but qualitatively it can be thought of as a distribution that occurs naturally and very frequently. 9. 5 cm on either side of 180 cm. 9 Histogram of a normal distribution, in this case the height of 334 fictitious people. The modal (most frequently occurring) bin is centered at 180 cm. This clearly looks like a Gaussian distribution; but in order to test this assumption, let’s use the formulaic definition of a Gaussian to see if we can fit this data through trial and error.

But how does the intelligent-algorithm practitioner evaluate their algorithm? This is of great importance for several reasons. First, without an objective evaluation, it’s not possible to track performance, and so it’s impossible to know if modifications have improved operation. Second, if you can’t measure performance, then justification becomes difficult. From a business perspective, managers and technologists will always try to measure value against cost, and being able to solidly evaluate your solution will help keep it in production!

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