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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

Product type Paperback

Published in Jul 2018

Publisher

ISBN-13 9781788623223

Length 406 pages

Edition 3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (3):

Luis Pedro Coelho

Willi Richert

Matthieu Brucher

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Table of Contents (17) Chapters

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

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Summary

We saw here that reinforcement learning is a very powerful tool to train a model on data where we don't have a ground truth or the optimal strategy. This still takes lots of time to achieve on a single processor, especially without GPU.

In Chapter 14, Bigger Data, we will see how we can harness the power of the cloud for even more complex models.

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Authors (3)

Willi Richert

Willi Richert is a Software Development Engineer at Microsoft for the last 5 years. He has also worked as a project team leader for mobile devices (Java/Python) at Richert GbR.

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Pedro Coelho

Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.

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Brucher

Matthieu Brucher is a computer scientist who specializes in high-performance computing and computational modeling and currently works for JPMorgan in their quantitative research branch. He is also the lead developer of Audio ToolKit, a library for real-time audio signal processing. He has a PhD in machine learning and signals processing from the University of Strasbourg, two Master of Science degreesone in digital electronics and signal processing and another in automation from the University of Paris XI and Supelec, as well as a Master of Music degree from Bath Spa University.

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