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Machine Learning for Mobile

You're reading from   Machine Learning for Mobile Practical guide to building intelligent mobile applications powered by machine learning

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Length 274 pages
Edition 1st Edition
Tools
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Authors (2):
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Avinash Venkateswarlu Avinash Venkateswarlu
Author Profile Icon Avinash Venkateswarlu
Avinash Venkateswarlu
Revathi Gopalakrishnan Revathi Gopalakrishnan
Author Profile Icon Revathi Gopalakrishnan
Revathi Gopalakrishnan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Machine Learning on Mobile FREE CHAPTER 2. Supervised and Unsupervised Learning Algorithms 3. Random Forest on iOS 4. TensorFlow Mobile in Android 5. Regression Using Core ML in iOS 6. The ML Kit SDK 7. Spam Message Detection 8. Fritz 9. Neural Networks on Mobile 10. Mobile Application Using Google Vision 11. The Future of ML on Mobile Applications 12. Question and Answers 13. Other Books You May Enjoy

TensorFlow Mobile in Android

In the previous chapter, we focused on supervised learning and unsupervised learning, and learned about the different types of learning algorithms. In this chapter, we will get introduced to TensorFlow for mobile, and go through a sample program implementation using TensorFlow for mobile. In Chapter 9, Neural Networks on Mobile, we will be using it to implement a classification algorithm. But we need to understand how TensorFlow for mobile works and be able to write samples using it before we can implement machine learning algorithms with it. The objective of this chapter is to get introduced to TensorFlow, TensorFlow Lite, TensorFlow for mobile, and their ways of working, and to try hands-on examples using TensorFlow for mobile in Android.

In this chapter, we will cover the following topics:

  • An introduction to TensorFlow, TensorFlow...
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