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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

Building an Artificial Intelligence Authentication System

Authentication is one of the most prominent features of any application, regardless of it being a native mobile software or a website, and has been an actively growing field since the onset of the need to protect data, and the need for privacy in relation to sensitive data, that is being shared on the internet. In this chapter, we will begin with a simple Firebase-based login to an application, and then improvise step by step to include an artificial intelligence (AI)-based authentication confidence metric, and ReCaptcha by Google. All these authentication methods use deep learning at their core and present a state-of-the-art method of implementing security in mobile apps.

In this chapter, we will cover the following topics: 

  • A simple login application
  • Adding Firebase authentication
  • Understanding anomaly detection...
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