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Machine Learning for Mobile

You're reading from  Machine Learning for Mobile

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Revathi Gopalakrishnan Revathi Gopalakrishnan
Profile icon Revathi Gopalakrishnan
Avinash Venkateswarlu Avinash Venkateswarlu
Profile icon Avinash Venkateswarlu
View More author details

Table of Contents (19) Chapters

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Introduction to Machine Learning on Mobile 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 1. Question and Answers 2. Other Books You May Enjoy Index

Summary


In this chapter, we learned about Fritz, an end-to-end platform that enables us to create machine learning applications. We also looked at pre-built ML models and how to use custom models in Fritz. Then, we explored how we can implement Fritz in Core ML in iOS and Android. Finally, we created two applications using the Fritz library: one using a pre-built fritz model, and the other using a Core ML model for iOS. In the next chapter, we will learn about neural networks and their uses for mobile applications and machine learning.

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