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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

Implementing ReCaptcha for spam protection

To add another layer of security to Firebase authentication, we will use ReCaptcha. This is a test supported by Google that helps us to protect data from automated bots for the purpose of spam and abuse. The test is simple and can be easily solved by humans, but obstructs bots and malicious users.

To read more about ReCaptcha and its uses, visit https://support.google.com/recaptcha/?hl=en.

ReCAPTCHA v2

In this section, we will be integrating ReCaptcha Version 2 into our application. In this version, the user is presented with a simple checkbox. If the tick turns green, the user has been verified.

Additionally, the user can also be presented with a challenge to distinguish between...

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