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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

Understanding recent trends in DL on mobile applications

DL specifically, and Artificial Intelligence (AI) more broadly, are becoming more and more mobile with the latest technology and hardware advancements. Organizations have been using intelligent algorithms to provide a personalized user experience and increase app engagement. With technologies such as face detection, image processing, text recognition, object recognition, and language translations, mobile applications have become much more than just a medium to provide static information. They are capable of adapting to the user's personal preferences and choices, as well as the present and past environment situations, to provide a seamless user experience.

Let's look at some trending applications and the methods they deploy to provide a good user experience, alongside increasing app engagement.

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