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)

Summary

In this chapter, we looked at some of the most common tools that are available for creating chatbots and then proceeded with an in-depth discussion of Dialogflow to understand the basic terminology that's used. We understood how the Dialogflow Console works so that we can create our own Dialogflow agent. We did this by creating an intent that's capable of extracting the user's name and adding it as an integration to Google Assistant so that it can respond with lucky numbers.

After deploying the webhook for Cloud Functions for Firebase and creating Actions on Google release, we created a conversational Flutter application. We learned how to create a conversation application interface and integrated the Dialogflow agent to facilitate deep learning models based on the responses of the chatbot. Finally, we used a Flutter plugin to add speech recognition...