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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

Concepts

Machine Learning

Authors (2):

Revathi Gopalakrishnan

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

Leave a review - let other readers know what you think

Index

Chapter 8. Fritz

We have gone through mobile machine learning SDKs offered by Google—TensorFlow for mobile—and Apple—Core ML—in the previous chapters and got a good understanding of them. We looked at the basic architecture of those products, the key features they offer, and also tried a few tasks/programs using those SDKs. Based on what we have explored on the mobile machine learning frameworks and tools so far, we will be able to identify a few gaps that make it difficult to carry out mobile machine learning deployments and subsequent maintenance and support of those deployments. Let me list a few for you:

Once we create the machine learning model and import it into the Android or iOS application, if there is any change that needs to be done to the model that was imported into the mobile application, how do you think this change will be implemented and upgraded to the application that is deployed and being used in the field? How is it possible to update/upgrade the model without redeploying...

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Authors (2)

Revathi Gopalakrishnan

Revathi Gopalakrishnan is a software professional with more than 17 years of experience in the IT industry. She has worked extensively in mobile application development and has played various roles, including developer and architect, and has led various enterprise mobile enablement initiatives for large organizations. She has also worked on a host of consumer applications for various customers around the globe. She has an interest in emerging areas, and machine learning is one of them. Through this book, she has tried to bring out how machine learning can make mobile application development more interesting and super cool. Revathi resides in Chennai and enjoys her weekends with her husband and her two lovely daughters.

See other products by Revathi Gopalakrishnan

Avinash Venkateswarlu

Avinash Venkateswarlu has more than 3 years' experience in IT and is currently exploring mobile machine learning. He has worked in enterprise mobile enablement projects and is interested in emerging technologies such as mobile machine learning and cryptocurrency. Venkateswarlu works in Chennai, but enjoys spending his weekends in his home town, Nellore. He likes to do farming or yoga when he is not in front of his laptop exploring emerging technologies.

See other products by Avinash Venkateswarlu

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