Download e-book for kindle: Algorithms of the Intelligent Web by Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

ISBN-10: 1617292583

ISBN-13: 9781617292583

Summary

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

Purchase of the print publication encompasses a loose book 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 functions. 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 computer studying purposes that crunch and wrangle information gathered from clients, net functions, and web site logs. during this absolutely revised version, you will examine clever algorithms that extract actual worth from facts. Key laptop studying recommendations are defined with code examples in Python's scikit-learn. This e-book courses you thru algorithms to trap, shop, and constitution information streams coming from the net. you are going to discover advice 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 knowledge technology practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computing device studying strategies for business strategies. Dmitry Babenko designs functions 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 info: clustering and remodeling your facts
  3. Recommending suitable content
  4. Classification: putting issues the place they belong
  5. Case examine: click on prediction for on-line advertising
  6. Deep studying and neural networks
  7. Making the proper 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

Sample text

10. If we now use the cumulative density function to calculate the probabilities of falling in the same bins, with parameters μ=180,σ=28, we can visually inspect how accurate the model is. 10 Fictional data on the height of 334 people. The sample probability of a given user occurring in a given height range is shown in red (the bar on the left in each pair). The probability from a Gaussian model, with parameters μ=180,σ =28, is shown in green (the bar on the right). 10 shows that our initial guess of 180 for the mean and 28 for the standard deviation isn’t bad.

Well, we’re here to tell you that all of your intelligent-algorithm problems can’t be solved with the same algorithm! Intelligent-application software is like every other piece of software—it has a certain area of applicability and certain limitations. Make sure you test your favorite solution thoroughly in new areas of application. In addition, it’s recommended that you examine every problem with a fresh perspective; a different algorithm may solve a different problem more efficiently or more expediently.

4 Returning to the previous example, women may be distributed normally with a lower average height than men, resulting in an underlying generating function drawn from two distributions! 11. 11 Probability distributions of height for men and women. Note that these probabilities are conditioned on the fact that gender is known: so, for example, given that we know a particular person is a woman, her probability of having a height in a particular bucket can be read off the y-axis. 4 Unlike the previous equation, where the parameters are explicitly represented on the left-hand side of the equation, in this case, the parameters are omitted and implied.

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


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