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TensorFlow Machine Learning Cookbook

You're reading from   TensorFlow Machine Learning Cookbook Over 60 recipes to build intelligent machine learning systems with the power of Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789131680
Length 422 pages
Edition 2nd Edition
Languages
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Authors (2):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Nick McClure Nick McClure
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Nick McClure
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with TensorFlow FREE CHAPTER 2. The TensorFlow Way 3. Linear Regression 4. Support Vector Machines 5. Nearest-Neighbor Methods 6. Neural Networks 7. Natural Language Processing 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Taking TensorFlow to Production 11. More with TensorFlow 12. Other Books You May Enjoy

Implementing a non-linear SVM

For this recipe, we will apply a non-linear kernel to split a dataset.

Getting ready

In this section, we will implement the preceding Gaussian kernel SVM on real data. We will load the iris dataset and create a classifier for I. setosa (versus Non-setosa). We will see the effect of various gamma values on the classification.

How to do it...

We proceed with the recipe as follows:

  1. We first load the necessary libraries, which includes the scikit-learn datasets so that we can load the iris data. Then, we will start a graph session. Use the following...
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