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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

Summary


In this chapter, we covered the concept of neural networks and their use in the field of mobile machine learning. We created an application to recognize images using TensorFlow and Core ML in iOS and Xcode. We also explored the Keras deep learning framework. We tried to solve the handwritten digit recognition problem using a neural network in Keras. We built the Keras machine learning model to solve this problem. Then, we converted this model into a Core ML model using Core ML conversion tools. We used this Core ML model in an iOS mobile application to perform the handwritten digit recognition.

In the next chapter, we will learn how to use the Google Cloud Vision label detection technique in Android.

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