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Mastering TensorFlow 1.x

You're reading from Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788292061

Length 474 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Table of Contents (21) Chapters

Preface

1. TensorFlow 101

2. High-Level Libraries for TensorFlow FREE CHAPTER

3. Keras 101

4. Classical Machine Learning with TensorFlow

5. Neural Networks and MLP with TensorFlow and Keras

6. RNN with TensorFlow and Keras

7. RNN for Time Series Data with TensorFlow and Keras

8. RNN for Text Data with TensorFlow and Keras

9. CNN with TensorFlow and Keras

10. Autoencoder with TensorFlow and Keras

11. TensorFlow Models in Production with TF Serving

12. Transfer Learning and Pre-Trained Models

13. Deep Reinforcement Learning

14. Generative Adversarial Networks

15. Distributed Models with TensorFlow Clusters

16. TensorFlow Models on Mobile and Embedded Platforms

17. TensorFlow and Keras in R

18. Debugging TensorFlow Models

19. Tensor Processing Units

20. Other Books You May Enjoy

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TensorFlow Serving on Kubernetes

According to https://kubernets.io:

Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.

TensorFlow models can be scaled to be served from hundreds or thousands of TF Serving services using Kubernetes clusters in the production environment. Kubernetes clusters can be run on all popular public clouds, such as GCP, AWS, Azure, as well as in your on-premises private cloud. So let us dive right in to learn to install Kubernetes and then deploy the MNIST model on Kubernetes Cluster.

Installing Kubernetes

We installed Kubernetes on Ubuntu 16.04 in a single-node local cluster mode as per the following steps:

Install LXD and Docker, which...

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