By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko


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

Purchase of the print booklet features a loose publication 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 net information trend extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you the way to create computer studying purposes that crunch and wrangle info amassed from clients, internet purposes, and site logs. during this absolutely revised variation, you will examine clever algorithms that extract actual worth from facts. Key computing device studying recommendations are defined with code examples in Python's scikit-learn. This ebook courses you thru algorithms to catch, shop, and constitution information streams coming from the internet. you are going to discover advice engines and dive into class 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 computer studying professional 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 innovations for business suggestions. Dmitry Babenko designs functions for banking, coverage, and supply-chain administration. Foreword by way of Yike Guo.

Table of Contents

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

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Algorithms of the Intelligent Web

Precis Algorithms of the clever net, moment variation teaches crucial ways to algorithmic internet information research, permitting you to create your personal laptop studying purposes that crunch, munge, and wrangle facts accumulated from clients, net purposes, sensors and site logs. buy of the print booklet incorporates a loose publication in PDF, Kindle, and ePub codecs from Manning courses.

Extra info for Algorithms of the Intelligent Web

Example text

For example, what if the data was collected early in the flower-growing season? What if the flowers were immature and hadn’t grown to their full size? This would mean the flowers are systematically different from the overall population—this is known as bias. Clusters generated from this data may be fine within the dataset being used, but due to their differences from the rest of the population, the clusters may not generalize well. Conversely, what if the data-collection task was delegated to many different people?

Second, subject to the curse of dimensionality (covered in chapter 2), getting more data to throw at a simple algorithm often yields results that are far superior to making your classifier more and more complicated. If you look at large corporations such as Google, which use massive amounts of data, an equal measure of achievement should be attributed to how they deal with large volumes of training data as well as the complexity and sophistication of their classification solutions. 4 Different algorithms have different scaling characteristics Don’t assume that an intelligent-application solution can scale just by adding more machines.

If this change drops below a certain tolerance value, you can safely assume that only a small number of points are being reassigned and thus the algorithm has converged. 3 K-means pseudo-code: expectation maximization Initialize centroids Expectation step while(centroids not converged): For each data item, assign label to that of closest centroid. Calculate the centroid using data items assigned to it Maximization step The expectation step assigns a centroid to every data item based on the Euclidean distance to that centroid (the closest is chosen).

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