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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

Neural Networks on Mobile

In Chapter 2, Supervised and Unsupervised Learning Algorithms, when we introduced you to TensorFlow, its components, and how it works, we talked briefly about convolutional neural networks (CNNs) and how they work. In this chapter, we will delve into the basic concepts of neural networks. We will explore the similarities and variations between machine learning and neural networks.

We will also go through some of the challenges of executing deep learning algorithms on mobile devices. We will briefly go through the various deep learning and neural network SDKs available for mobile applications that can be run on mobile devices directly. Toward the end of this chapter, we will create an interesting assignment that will utilize both TensorFlow and Core ML. 

In this chapter, we will be cover the following topics:

  • Creating a TensorFlow...
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