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

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By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko


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

Purchase of the print e-book contains 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 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 facts development extraction.

About the Book

Algorithms of the clever internet, moment Edition teaches you ways to create computer studying functions that crunch and wrangle facts accumulated from clients, internet functions, and site logs. during this completely revised variation, you are going to examine clever algorithms that extract genuine worth from facts. Key desktop studying options are defined with code examples in Python's scikit-learn. This ebook 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 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 computing device studying specialist and information technological know-how practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of computer studying concepts for commercial recommendations. Dmitry Babenko designs purposes for banking, assurance, and supply-chain administration. Foreword via Yike Guo.

Table of Contents

  1. Building functions for the clever web
  2. Extracting constitution from info: clustering and remodeling your facts
  3. Recommending proper 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 correct choice
  8. The way forward for the clever web
  9. Appendix - taking pictures information at the web

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Extra resources for Algorithms of the Intelligent Web

Example text

Now, for each data point in the dataset, we’ll assign that point to the nearest cluster. 5 will be assigned to cluster k2. 5 shows this with the data points belonging to k1 shaded green (diamonds, in the print version of this book) and those belonging to k2 shaded red (squares). Not a bad start, you might think, but we can do better. If we revisit the data, we see that it looks more like points below 7 belong to the leftmost cluster, k1, and points above 7 belong to the rightmost cluster, k2. 4 Initial assignment of the means of the k clusters.

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. The two-dimensional plots are color-coded such that the color-coding is consistent across the plots.

This makes it extremely unlikely that for any moderate-sized feature set you’ll see even a decent fraction of the total possible inputs. More worrisome, your intuition breaks down as the number of features increases. 20 Building simple classifiers in a low number of dimensions is easy, but increasing the number of dimensions makes it hard to understand what’s happening. ” This is especially important when it comes to building solutions using ML approaches. Understanding the problem domain is key here, and deriving a set of features that exposes the underlying phenomena under investigation can mean all the difference to classification accuracy and generalization.

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