By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

ISBN-10: 1617292583

ISBN-13: 9781617292583


Algorithms of the clever internet, moment Edition teaches crucial ways to algorithmic net facts research, permitting you to create your individual computer studying functions that crunch, munge, and wrangle info accrued from clients, internet functions, sensors and site logs.

Purchase of the print publication features a unfastened e-book in PDF, Kindle, and ePub codecs from Manning Publications.

About the Technology

Valuable insights are buried within the tracks internet clients go away as they navigate pages and purposes. you could discover them by utilizing clever algorithms just like the ones that experience earned fb, Google, and Twitter a spot one of the giants of internet information trend extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you ways to create desktop studying functions that crunch and wrangle info gathered from clients, internet purposes, and site logs. during this completely revised variation, you are going to examine clever algorithms that extract genuine worth from info. Key computing device studying ideas are defined with code examples in Python's scikit-learn. This booklet publications you thru algorithms to catch, shop, and constitution information streams coming from the internet. you are going to discover suggestion engines and dive into class 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 suggestion engines work

About the Reader

Knowledge of Python is assumed.

About the Authors

Douglas McIlwraith is a laptop studying specialist and information technology practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computer studying suggestions for commercial options. Dmitry Babenko designs functions for banking, coverage, and supply-chain administration. Foreword via Yike Guo.

Table of Contents

  1. Building purposes for the clever web
  2. Extracting constitution from info: clustering and reworking your information
  3. Recommending suitable content
  4. Classification: putting issues the place they belong
  5. Case research: 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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Extra info for Algorithms of the Intelligent Web

Sample text

Gottfredson, “Mainstream Science on Intelligence: An Editorial with 52 Signatories, History, and Bibliography,” Wall Street Journal, December 13, 1994. 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).

In the final section, we’ll review an important method for investigating the structure of data and for reducing the total number of features in your dataset without sacrificing the information contained in your data. This topic is equivalent to understanding how your variables vary together in a data set. 1, it’s equivalent to understanding that x increases with y regardless of the class of data. The method we’ll cover is known as principal component analysis (PCA). Used on its own, this algorithm uncovers the principal directions of variance in your data—helping you understand which of your features are important and which are not.

As as the dimensionality continues to increase, the ratio of the minimum distance over the maximum distance approaches the value of 1. This means no matter which direction you look and what distance you measure, it all looks the same! What that this means practically for you is that the more attributes of the data you collect, the bigger the space of possible points is; it also means the similarity of these points becomes harder to determine. In this chapter, we’re looking at the structure of data, and in order to determine patterns and structure in the data, you might think it’s useful to collect lots of attributes about the phenomenon under investigation.

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

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