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

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

Summary

Algorithms of the clever internet, moment Edition teaches crucial ways to algorithmic net information research, allowing you to create your personal computing device studying functions that crunch, munge, and wrangle facts accrued from clients, internet purposes, sensors and site logs.

Purchase of the print booklet encompasses a loose booklet 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 through the use of clever algorithms just like the ones that experience earned fb, Google, and Twitter a spot one of the giants of internet info trend extraction.

About the Book

Algorithms of the clever net, moment Edition teaches you the way to create laptop studying functions that crunch and wrangle information accrued from clients, internet functions, and site logs. during this absolutely revised version, you will examine clever algorithms that extract actual price from facts. Key computing device studying strategies are defined with code examples in Python's scikit-learn. This e-book publications you thru algorithms to trap, shop, and constitution info streams coming from the internet. you will discover suggestion engines and dive into type through statistical algorithms, neural networks, and deep learning.

What's Inside

  • Introduction to computing device 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 desktop 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 recommendations for commercial options. 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 information: clustering and remodeling your facts
  3. Recommending correct 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 the perfect choice
  8. The way forward for the clever web
  9. Appendix - shooting facts at the web

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Additional info for Algorithms of the Intelligent Web

Example text

Many believe that future classifiers will become more accurate through such techniques, but it does provide an additional layer of indirection for the non-expert to understand the mechanics of such a system. ”21 Just because two variables are correlated, this doesn’t imply causation between the two. ) variable that isn’t observable and is at play. Correlation should be taken as a sign of potential causation, warranting further investigation. org/about/open-letter. 8 19 Summary  We provided the 50,000–foot view of intelligent algorithms, providing many      specific examples based on real-world problems.

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. The probability density of the normal distribution is given by the following equation: 2 (x – μ) – ------------------2 2σ 1 2 f ( x μ ,σ ) = ----------------- e 2 2σ π This has two parameters, which we need to learn in order to fit this equation to the data: the mean given by the parameter μ and the standard deviation given by the parameter σ.

Gaussian mixtures, however, don’t suffer from such a constraint. This is because they seek to model feature covariance for each cluster. If this all seems a little confusing, don’t worry! We’ll go through these concepts in much more detail in the following sections. 34 Extracting structure from data: clustering and transforming your data What is the Gaussian distribution? 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.

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