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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher
ISBN-13 9781788623223
Length 406 pages
Edition 3rd Edition
Languages
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Authors (3):
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Luis Pedro Coelho Luis Pedro Coelho
Author Profile Icon Luis Pedro Coelho
Luis Pedro Coelho
Willi Richert Willi Richert
Author Profile Icon Willi Richert
Willi Richert
Matthieu Brucher Matthieu Brucher
Author Profile Icon Matthieu Brucher
Matthieu Brucher
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Machine Learning FREE CHAPTER 2. Classifying with Real-World Examples 3. Regression 4. Classification I – Detecting Poor Answers 5. Dimensionality Reduction 6. Clustering – Finding Related Posts 7. Recommendations 8. Artificial Neural Networks and Deep Learning 9. Classification II – Sentiment Analysis 10. Topic Modeling 11. Classification III – Music Genre Classification 12. Computer Vision 13. Reinforcement Learning 14. Bigger Data 15. Where to Learn More About Machine Learning 16. Other Books You May Enjoy

Learning about big data

The expression big data does not mean a specific amount of data, neither in the number of examples nor in the number of gigabytes, terabytes, or petabytes occupied by the data. It means that data has been growing faster than processing power. This implies the following:

  • Some of the methods and techniques that worked well in the past now need to be redone or replaced as they do not scale well to the new size of the input data
  • Algorithms cannot assume that all the input data can fit in memory
  • Managing data becomes a major task in itself
  • Using computer clusters or multicore machines becomes a necessity and not a luxury

This chapter will focus on this last piece of the puzzle: how to use multiple cores (either on the same machine or on separate machines) to speed up and organize your computations. This will also be useful in other medium-sized data tasks...

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