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TensorFlow 1.x Deep Learning Cookbook

You're reading from TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

Product type Paperback

Published in Dec 2017

Publisher Packt

ISBN-13 9781788293594

Length 536 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (2):

Dr. Amita Kapoor

Antonio Gulli

View More author details

Table of Contents (15) Chapters

Preface

1. TensorFlow - An Introduction

2. Regression FREE CHAPTER

3. Neural Networks - Perceptron

4. Convolutional Neural Networks

5. Advanced Convolutional Neural Networks

6. Recurrent Neural Networks

7. Unsupervised Learning

8. Autoencoders

9. Reinforcement Learning

10. Mobile Computation

11. Generative Models and CapsNet

12. Distributed TensorFlow and Cloud Deep Learning

13. Learning to Learn with AutoML (Meta-Learning)

14. TensorFlow Processing Units

Components of TPUs

Playing with Distributed TensorFlow: multiple servers

In this recipe, we will learn how to distribute a TensorFlow computation across multiple servers. The key assumption is that the code is the same for both the workers and the parameter servers. Therefore the role of each computation node is passed a command line argument.

Getting ready

Again, this recipe is inspired by a good blog posting written by Neil Tenenholtz and available online: https://clindatsci.com/blog/2017/5/31/distributed-tensorflow

How to do it...

We proceed with the recipe as follows:

Consider this...

The rest of the chapter is locked

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