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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Building a face recognizer using a local binary patterns histogram

We are now ready to build a face recognizer. We need a face dataset for training, so we've provided you with a folder called faces_dataset that contains a small number of images that are sufficient for training. This dataset is a subset of the dataset that is available at http://www.vision.caltech.edu/Image_Datasets/faces/faces.tar. This dataset contains a good number of images that we can use to train a face recognition system.

We will use a local binary patterns histogram to build our face recognition system. In our dataset, you will see different people. Our job is to build a system that can learn to separate these people from one another. When we see an unknown image, our system will assign it to one of the existing classes.

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