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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Summary

In this chapter, we studied super-resolution images and how we can apply them using a SRGAN. We also studied other types of GANs and how GANs work in general. Then, we discussed how to create a Flutter application that can be integrated with an API hosted on a DigitalOcean Droplet so that we can perform image super-resolution when an image has been picked from the gallery. Next, we covered how to use DigitalOcean Droplets and how it is a good choice for hosting the backends of applications due to its low cost and easy-to-use interface.

In the next chapter, we will discuss some popular applications that have seen great improvements by integrating deep learning into their functionality. We will also explore some hot research areas in deep learning for mobile phones, and briefly discuss the latest work that has been done on them.

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