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Large Scale Machine Learning with Python

You're reading from   Large Scale Machine Learning with Python Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785887215
Length 420 pages
Edition 1st Edition
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Authors (3):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Bastiaan Sjardin Bastiaan Sjardin
Author Profile Icon Bastiaan Sjardin
Bastiaan Sjardin
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
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Table of Contents (12) Chapters Close

Preface 1. First Steps to Scalability FREE CHAPTER 2. Scalable Learning in Scikit-learn 3. Fast SVM Implementations 4. Neural Networks and Deep Learning 5. Deep Learning with TensorFlow 6. Classification and Regression Trees at Scale 7. Unsupervised Learning at Scale 8. Distributed Environments – Hadoop and Spark 9. Practical Machine Learning with Spark A. Introduction to GPUs and Theano Index

Streaming data from sources

Some data is really streaming through your computer when you have a generative process that transmits data, which you can process on the fly or just discard, but not recall afterward unless you have stored it away in some data archival repository somewhere. It is like dragging water from a flowing river—the river keeps on flowing but you can filter and process all the water as it goes. It's a completely different strategy from processing all the data at once, which is more like putting all the water in a dam (an analogy for working with all the data in-memory).

As an example of streaming, we could quote the data flow produced instant by instant by a sensor or, even more simply, a Twitter streamline of tweets. Generally, the main sources of data streams are as follows:

  • Environment sensors measuring temperature, pressure, and humidity
  • GPS tracking sensors recording the location (latitude/longitude)
  • Satellites recording image data
  • Surveillance videos and...
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