By Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko
Algorithms of the clever internet, moment Edition teaches an important methods to algorithmic net facts research, allowing you to create your personal desktop studying purposes that crunch, munge, and wrangle info gathered from clients, net functions, sensors and site logs.
Purchase of the print e-book features a unfastened publication in PDF, Kindle, and ePub codecs from Manning Publications.
About the Technology
Valuable insights are buried within the tracks net 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 information development extraction.
About the Book
Algorithms of the clever internet, moment Edition teaches you ways to create laptop studying purposes that crunch and wrangle information amassed from clients, internet functions, and web site logs. during this completely revised variation, you will examine clever algorithms that extract genuine price from information. Key desktop studying options are defined with code examples in Python's scikit-learn. This ebook courses you thru algorithms to seize, shop, and constitution facts streams coming from the internet. you will discover advice engines and dive into type through statistical algorithms, neural networks, and deep learning.
- Introduction to desktop 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 desktop studying professional and knowledge technological know-how practitioner within the box of web advertising. Dr. Haralambos Marmanis is a pioneer within the adoption of laptop studying ideas for business ideas. Dmitry Babenko designs functions for banking, coverage, and supply-chain administration. Foreword through Yike Guo.
Table of Contents
- Building purposes for the clever web
- Extracting constitution from facts: clustering and reworking your info
- Recommending correct content
- Classification: putting issues the place they belong
- Case learn: click on prediction for on-line advertising
- Deep studying and neural networks
- Making the suitable choice
- The way forward for the clever web
- Appendix - shooting information at the web
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Additional info for Algorithms of the Intelligent Web
Some numeric datasets are discrete (such as the number of people in a room), and some are continuous (for example, the temperature or the atmospheric pressure). 2 Inference does not happen instantaneously Computing a solution takes time, and the responsiveness of your application may be crucial for the financial success of your business. You shouldn’t assume that all algorithms on all datasets will run within the response-time limits of your application. You should test the performance of your algorithm within the range of your operating characteristics.
A. gov) :Date: July, 1988 Using the DESCR attribute, you can obtain and print to the console some information that has been recorded about the Iris dataset. data, the features consist of the sepal length (cm), the sepal width (cm), the petal length (cm), and the petal width (cm), in that order. target, are Iris-Setosa, Iris-Versicolour, and Iris-Virginica. 1, you see that these are encoded by integers. It’s easy to understand which class names relate to which integer class labels by using the target_names attribute, which stores the values in the same order in which they’re encoded.
This key concept will be revisited again in this chapter when we discuss our second clustering method using the Gaussian mixture model (GMM). Using a GMM to cluster can be seen as an extension of the k-means algorithm. It’s possible to achieve exactly the same results with GMM and k-means if you provide certain limitations to your GMM. We’ll discuss this in much more detail in the coming sections, but for now, you can think of GMM as a slightly more expressive model that assumes and captures some additional information about the distribution of data points.