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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 FREE CHAPTER

2. Regression

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

Training a Distributed TensorFlow MNIST classifier

This recipe is used to train a full MNIST classifier in a distributed way. This recipe is inspired by the blog post in http://ischlag.github.io/2016/06/12/async-distributed-tensorflow/ and the code running on TensorFlow 1.2 is available here https://github.com/ischlag/distributed-tensorflow-example

Getting ready

This recipe is based on the previous one. So it might be convenient to read them in order.

How to do it...

We proceed with the recipe as follows:

Import a few standard modules and define the TensorFlow cluster...

The rest of the chapter is locked

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