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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

The ML Kit SDK

In this chapter, we will discuss ML Kit, which was announced by Firebase at the Google I/O 2018. This SDK packages Google's mobile machine learning offerings under a single umbrella.

Mobile application developers may want to implement features in their mobile apps that require machine learning capabilities. However, they may not have knowledge of machine learning concepts and which algorithms to use for which scenarios, how to build the model, train the model, and so on.

ML Kit tries to address this problem by identifying all the potential use cases for machine learning in the context of mobile devices, and providing ready-made APIs. If the correct inputs are passed to these, the required output is received, with no further coding required.

Additionally, this kit enables the inputs to be passed either to on-device APIs that work offline, or to online APIs that...

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