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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

Learning the TensorFlow way of linear regression

While using matrices and decomposition methods are very powerful, TensorFlow has another way to solve for the slope and intercept. TensorFlow can do this iteratively, gradually learning the best linear regression parameters that will minimize the loss.

Getting ready

In this recipe, we will loop through batches of data points and let TensorFlow update the slope and y intercept. Instead of generated data, we will use the iris dataset that is built into the scikit-learn library. Specifically, we will find an optimal line through data points where the x value is the petal width and the y value is the sepal length. We chose these two because there appears to be a linear relationship...

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