Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Rimjhim Bhadani
Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By: Anubhav Singh, Rimjhim Bhadani

Overview of this book

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.
Table of Contents (13 chapters)

Creating an Action on Google release

Finally, we're at the stage where we can create a release for our Actions on Google chatbot. But before doing so, it is important to test out the chatbot in the Google Assistant Test Simulator:

  1. Click on the Simulator button on the left navigation pane of the Actions on Google Console to enter the simulator. In the simulator, you'll be shown an interface similar to using Google Assistant on your phone. A suggested input will contain the invocation method for your action. 


  1. Enter the invocation for your action in the simulator, which in our case is Talk to Peter Please. This will produce an output from the Default Welcome Intent asking for your name. Upon entering your name as the response, similar to My name is Sammy, you'll be shown your lucky number, like this: 

Now that we know our chatbot is...