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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

Understanding the basics of Core ML

Core ML enables iOS mobile applications to run machine learning models locally on a mobile device. It enables developers to integrate a broad variety of machine learning model types into a mobile application. Developers do not require extensive knowledge of machine learning or deep learning to write machine learning mobile applications using Core ML. They just need to know how to include the ML model into the mobile app similar to other resources and use invoke it in the mobile application. A data scientist or a machine learning expert can create an ML model in any technology they are familiar with, say Keras, scikit-learn, and so on. Core ML provides tools to convert the ML data model created using other tools (tensor, scikit-learn, and so on) to a format that is mandated by Core ML. 

This conversion to a Core ML model...

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