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

By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko

Summary

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

Purchase of the print ebook incorporates a unfastened publication 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 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 development extraction.

About the Book

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

What's Inside

  • Introduction to computer 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 professional and information technology practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computing device studying options for business ideas. Dmitry Babenko designs functions for banking, coverage, and supply-chain administration. Foreword through 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 the ideal choice
  8. The way forward for the clever web
  9. Appendix - shooting facts at the web

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Extra resources for Algorithms of the Intelligent Web

Sample text

When a cost function is minimized, practitioners say that the training algorithm has converged. In this book, we’ll mostly rely on scikitlearn libraries to reach convergence for us during training. 30 CHAPTER 2 Extracting structure from data: clustering and transforming your data Note that this final assignment is exactly the means we would have chosen if we had manually performed clustering (that is, we visually determined the clusters and calculated their means); thus the algorithm has automatically determined an intelligent clustering of the data with minimal intervention from the developer!

You’ll see that, yes, the algorithm does indeed separate the data into three clusters! This is great, but you’d learn more if you could look at these clusters in comparison to the data. The next listing provides the code to visualize this. 8. Color and shape of the data points denote the cluster to which the point belongs—as discovered by k-means. In this figure, all combinations of the Iris features are plotted against each other in order to assess the quality of clustering. Note that each point is assigned to the cluster with the centroid closest to it in four-dimensional space.

Represent this, new means are calculated based on the average values in that cluster: that is, the new mean for cluster k1 is given by the mean of the green data points, and the new mean for cluster k2 is given by the mean of the red data points. 2, respectively. 6. Essentially, both cluster means have been dragged up by the data points assigned to them. We now proceed with the second iteration. As before, for each data point, we assign it to the cluster with the closest centroid. 5 Initial assignment of data points to clusters.

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