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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

Writing the mobile application using the TensorFlow model

What we are going to do?

In this section, we are going to build a small (a+b)2 model in TensorFlow, deploy it into an android mobile application, and run it from the Android mobile device.

What do you need to know?

To proceed in this section, you need a working installation of Python, TensorFlow dependencies, and android studio, and also some knowledge of python and java android. You can find the instructions on how to install TensorFlow here: https://www.tensorflow.org/install/.

If you need a detailed installation procedure for Windows, please refer to the one provided with screenshots in the Chapter 11, The Future of ML on Mobile Applications of this book.

We saw the details of TensorFlow already. To put it onto a simple words TensorFlow is nothing but saving the tensor flow program written...

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