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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 nearest-neighbors

We will start this chapter by implementing nearest-neighbors to predict housing values. This is a great way to start with nearest-neighbors, because we will be dealing with numerical features and continuous targets.

Getting ready

To illustrate how making predictions with nearest-neighbors works in TensorFlow, we will use the Boston housing dataset. Here, we will be predicting the median neighborhood housing value as a function of several features.

Since we consider the training set the trained model, we will find the k-NNs to the prediction points, and will calculate a weighted average of the target value.

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