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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

Deploying the model in Flutter

At this point, we have our Firebase authentication application running along with ReCaptcha protection. Now, let's add the final layer of security that won't allow any malicious users to enter the application.

We already know that the model is hosted at the endpoint: http://34.67.126.237:8000/login. We will simply make an API call from within the application, passing in the email and password provided by the user, and get the result value from the model. The value will assist us in judging whether the login was malicious by using a threshold result value.

If the value is less than 0.20, the login will be considered malicious and the following message will be shown on the screen:

Let's now look at the steps to deploy the model in the Flutter application:

  1. First of all, since we are fetching data and will be using network calls, that...
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