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TensorFlow Machine Learning Cookbook

You're reading from TensorFlow Machine Learning Cookbook Over 60 practical recipes to help you master Google's TensorFlow machine learning library

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781786462169

Length 370 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Nick McClure

View More author details

Table of Contents (13) Chapters

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

Index

Chapter 5. Nearest Neighbor Methods

This chapter will focus on nearest neighbor methods and how to implement them in TensorFlow. We will start with an introduction to the method and show how to implement various forms, and the chapter will end with examples of address matching and image recognition. This is what we will cover:

Working with Nearest Neighbors
Working with Text-Based Distances
Computing Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition

Note that all the code is available online at https://github.com/nfmcclure/tensorflow_cookbook.

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Authors (1)

Nick McClure

Nick McClure

Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow Group and Caesar's Entertainment Corporation. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog or through his Twitter account, @nfmcclure.

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