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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

Introduction

Nearest-neighbor methods are rooted in a distance-based conceptual idea. We consider our training set a model, and make predictions on new points based on how close they are to points in the training set. A naive method is to make the prediction class the same as the closest training data point class. But since most datasets contain a degree of noise, a more common method is to take a weighted average of a set of k-nearest-neighbors. This method is called k-nearest-neighbors (k-NN).

Given a training dataset (x1,x2.....xn) with corresponding targets (y1, y2....yn), we can make a prediction on a point, z, by looking at a set of nearest-neighbors. The actual method of prediction depends on whether we are performing regression (continuous ) or classification (discrete ).

For discrete classification targets, the prediction can be given by a maximum voting scheme, weighted...

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