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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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781789131680

Length 422 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (2):

Nick McClure

Sujit Pal

View More author details

Table of Contents (19) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

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

1. Other Books You May Enjoy

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Index

Using multiple executors

You will be aware that there are many features of TensorFlow, including computational graphs that lend themselves naturally to being computed in parallel. Computational graphs can be split over different processors as well as in processing different batches. We will address how to access different processors on the same machine in this recipe.

Getting ready

For this recipe, we will show you how to access multiple devices on the same system and train on them. This is a very common occurrence: along with a CPU, a machine may have one or more GPUs that can share the computational load. If TensorFlow can access these devices, it will automatically distribute the computations to multiple devices via a greedy process. However, TensorFlow also allows the program to specify which operations will be on which device via a name scope placement.

In order to access GPU devices, the GPU version of TensorFlow must be installed. To install the GPU version of TensorFlow, visit https...

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Authors (2)

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.

See other products by Nick McClure

Sujit Pal

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

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