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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 2. Supervised and Unsupervised Learning Algorithms FREE CHAPTER 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

Key innovation areas


The following sections detail some of the business areas where innovation is happening, leveraging the power of ML. A number of players are already leading the way in this regard.

Personalization applications

Understanding user behavior by leveraging various parameters that are provided through mobile devices and understanding their life patterns for the purposes of personalization will be of value to users. When the same mobile application is going to cater to user profiles across a broad spectrum, it will be of significant value if it could provide specific features that best suit the person using it. Such advanced personalization could be brought into applications by leveraging ML.

Healthcare

Here, there are various use cases that help track various health parameters that can be tracked, learned, and put into use for providing innovations in healthcare, such as diagnostic applications that can diagnose based on pictures and sound from mobile applications.

Fitness tracking...

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