Download e-book for iPad: 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


Algorithms of the clever internet, moment Edition teaches an important ways to algorithmic internet information research, permitting you to create your personal laptop studying functions that crunch, munge, and wrangle info gathered from clients, internet functions, sensors and web site logs.

Purchase of the print e-book encompasses a loose e-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 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 facts trend extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you ways to create computing device studying functions that crunch and wrangle facts accumulated from clients, internet purposes, and site logs. during this absolutely revised version, you are going to examine clever algorithms that extract actual worth from information. Key laptop studying suggestions are defined with code examples in Python's scikit-learn. This booklet publications you thru algorithms to trap, shop, and constitution info streams coming from the net. you are going to discover advice engines and dive into class through statistical algorithms, neural networks, and deep learning.

What's Inside

  • Introduction to laptop 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 laptop studying specialist and information technological know-how practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computing device studying ideas for business ideas. Dmitry Babenko designs functions for banking, assurance, and supply-chain administration. Foreword by means of Yike Guo.

Table of Contents

  1. Building functions for the clever web
  2. Extracting constitution from info: clustering and remodeling your info
  3. Recommending appropriate 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 best choice
  8. The way forward for the clever web
  9. Appendix - shooting facts at the web

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Additional resources for Algorithms of the Intelligent Web

Example text

1 presents a demonstration (toy) dataset that will be used to illustrate how the performance of a predictor can be calculated. Features of the data are labeled with letters of the alphabet, and the ground truth and predicted (Boolean) classes are presented alongside. Imagine that predictions have been generated from a test set that the original model hasn’t been exposed to. This test set has had the ground truth withheld, and so the model must base its output on the value of the features alone.

Target_names array(['setosa', 'versicolour', 'virginica'], dtype='|S10') Although we’re working with a simple dataset, the concepts that you can learn here are universal among machine-learning algorithms. You’ll see later in this chapter how you can draw on machine learning to extract the structure of the data and how different techniques can be adopted to achieve this. To illustrate the concept of structure, let’s try some thought experiments with the Iris dataset. It may be that all Virginicas are much bigger than the other flowers, such that the lengths and widths of the sepals and petals have significantly higher values.

Although conceptually simple, the k-means algorithm is widely used in practice, and we’ll provide you with all the required code to get your very own implementation off the ground using scikit-learn. As part of our discussion of k-means, you’ll see how the clusters are learned through the use of an iterative training algorithm called 22 CHAPTER 2 Extracting structure from data: clustering and transforming your data expectation maximization (EM). This key concept will be revisited again in this chapter when we discuss our second clustering method using the Gaussian mixture model (GMM).

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

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