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You're reading from TensorFlow Machine Learning Projects Build 13 real-world projects with advanced numerical computations using the Python ecosystem

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781789132212

Length 322 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (2):

Ankit Jain

Dr. Amita Kapoor

View More author details

Table of Contents (17) Chapters

Preface

1. Overview of TensorFlow and Machine Learning FREE CHAPTER

2. Using Machine Learning to Detect Exoplanets in Outer Space

3. Sentiment Analysis in Your Browser Using TensorFlow.js

4. Digit Classification Using TensorFlow Lite

5. Speech to Text and Topic Extraction Using NLP

6. Predicting Stock Prices using Gaussian Process Regression

7. Credit Card Fraud Detection using Autoencoders

8. Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks

9. Generating Matching Shoe Bags from Shoe Images Using DiscoGANs

10. Classifying Clothing Images using Capsule Networks

11. Making Quality Product Recommendations Using TensorFlow

12. Object Detection at a Large Scale with TensorFlow

13. Generating Book Scripts Using LSTMs

14. Playing Pacman Using Deep Reinforcement Learning

15. What is Next?

16. Other Books You May Enjoy

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Learning about TensorFlowOnSpark

In the year 2016, Yahoo open sourced TensorFlowOnSpark, a Python framework for performing TensorFlow-based distributed deep learning on Spark clusters. Since then, it has undergone a lot of developmental changes and is one of the most active repositories regarding the distributed deep learning framework.

The TensorFlowOnSpark (TFoS) framework allows you to run distributed TensorFlow applications from within Spark programs. It runs on the existing Spark and Hadoop clusters. It can use existing Spark libraries such as SparkSQL or MLlib (the Spark machine learning library).

TFoS is automatic, so we do not need to define the nodes as PS nodes, nor do we need to upload the same code to all of the nodes in the cluster. By just performing a few modifications, we can run our existing TensorFlow code. It allows us to scale up the existing TensorFlow apps with minimal changes. It supports all of the existing TensorFlow functionality such as synchronous/asynchronous training...