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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

Working with a linear SVM

For this example, we will create a linear separator from the iris dataset. We know from prior chapters that the sepal length and petal width create a linear separable binary dataset for predicting whether a flower is I. setosa or not.

Getting ready

To implement a soft separable SVM in TensorFlow, we will implement the specific loss function, as follows:

Here, A is the vector of partial slopes, b is the intercept, is a vector of inputs, is the actual class, (-1 or 1), and is the soft separability regularization parameter.

How to do it...

We...

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