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

Description

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter Key Features Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing Cover interesting deep learning solutions for mobile Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook Description 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. What you will learn Create your own customized chatbot by extending the functionality of Google Assistant Improve learning accuracy with the help of features available on mobile devices Perform visual recognition tasks using image processing Use augmented reality to generate captions for a camera feed Authenticate users and create a mechanism to identify rare and suspicious user interactions Develop a chess engine based on deep reinforcement learning Explore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applicationsWho this book is for This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app's user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book. Table of Contents Introduction to Deep Learning for Mobile Mobile Vision : Face Detection using on-device models Chatbot using Actions on Google Recognizing Plant Species Live Captions Generation of Camera Feed Building Artificial Intelligence Authentication System Speech/Multimedia Processing: Generating music using AI Reinforced Neural Network based Chess Engine Building Image Super-Resolution Application Road Ahead Appendix Cover Title Page Copyright and Credits About Packt Contributors Table of Contents Preface Chapter 01: Introduction to Deep Learning for Mobile Growth of AI-powered mobile devices Changes in hardware to support AI Why do mobile devices need to have AI chips? Improved user experience with AI on mobile devices Personalization Virtual assistants Facial recognition AI-powered cameras Predictive text Most popular mobile applications that use AI Netflix Seeing AI Allo English Language Speech Assistant Socratic Understanding machine learning and deep learning Understanding machine learning Understanding deep learning The input layer The hidden layers The output layer The activation function Introducing some common deep learning architectures Convolutional neural networks Generative adversarial networks Recurrent neural networks Long short-term memory Introducing reinforcement learning and NLP Reinforcement learning NLP Methods of integrating AI on Android and iOS Firebase ML Kit Core ML Caffe2 TensorFlow Summary Chapter 02: Mobile Vision - Face Detection Using On-Device Models Technical requirements Introduction to image processing Understanding images Manipulating images Rotation Grayscale conversion Developing a face detection application using Flutter Adding the pub dependencies Building the application Creating the first screen Building the row title Building the row with button widgets Creating the whole user interface Creating the second screen Getting the image file Analyzing the image to detect faces Marking the detected faces Displaying the final image on the screen Creating the final MaterialApp Summary Chapter 03: Chatbot Using Actions on Google Technical requirements Understanding the tools available for creating chatbots Wit.ai Dialogflow How does Dialogflow work?  Creating a Dialogflow account Creating a Dialogflow agent Understanding the Dialogflow Console Creating an Intent and grabbing entities Creating your first action on Google Why would you want to build an action on Google? Creating Actions on a Google project Creating an integration to the Google Assistant Implementing a Webhook Deploying a webhook to Cloud Functions for Firebase Creating an Action on Google release Creating the UI for the conversational application Creating the Text Controller Creating ChatMessage Integrating the Dialogflow agent Adding audio interactions with the assistant Adding the plugin Adding SpeechRecognition Adding the mic button Summary Chapter 04: Recognizing Plant Species Technical requirements Introducing image classification Understanding the project architecture Introducing the Cloud Vision API Configuring the Cloud Vision API for image recognition Enabling the Cloud Vision API Creating a Cloud Vision API key Using an SDK/tools to build a model Introducing Google's Colaboratory Creating a custom TensorFlow Lite model for image recognition Creating a Flutter application Choosing between two different models Creating the second screen Creating the user interface Adding the functionality Displaying the chosen image on the screen Running image recognition Using the Cloud Vision API Using an on-device TensorFlow Lite model Updating the UI with results Summary Chapter 05: Generating Live Captions from a Camera Feed Designing the project architecture Understanding an image caption generator Understanding the dataset Building an image caption generation model Initializing the caption dataset Preparing the caption dataset Training Testing Creating a simple click-deploy image caption generation model Understanding the camera plugin Installing the camera plugin Adding methods for persistent storage and proper execution Coding Creating a camera application Building the camera preview Generating image captions from the camera feed Creating the material app Summary Chapter 06: Building an Artificial Intelligence Authentication System Technical requirements A simple login application Creating the UI Adding Firebase authentication Creating auth.dart Adding authentication in SignupSigninScreen Creating the main screen Creating the home screen Creating main.dart Understanding anomaly detection for authentication A custom model for authenticating users Building a model for an authentication validity check Hosting the custom authentication validation model Implementing ReCaptcha for spam protection ReCAPTCHA v2 Obtaining the API key Code integration Deploying the model in Flutter Summary Chapter 07: Speech/Multimedia Processing - Generating Music Using AI Designing the project's architecture Understanding multimedia processing Image processing Audio processing Magenta Video processing  Developing RNN-based models for music generation Creating the LSTM-based model Deploying a model using Flask Deploying an audio generation API on Android and iOS Creating the UI Adding Audio Player Deploying the model Creating the final material app Summary Chapter 08: Reinforced Neural Network-Based Chess Engine Introduction to reinforcement learning Reinforcement learning in mobile games Exploring Google's DeepMind AlphaGo Alpha Zero Monte Carlo tree search Alpha Zero-like AI for Connect 4 Creating a virtual representation of the board Allowing moves according to the game's rules The state management system Facilitating gameplay Generating sample gameplays System training Monte Carlo tree search implementation Implementing the neural network Underlying project architecture Developing a GCP-hosted REST API for the chess engine Understanding the Universal Chess Interface Deployment on GCP Request for a quota increase on GPU instances Creating a GPU instance Deploying the script Creating a simple chess UI on Android Adding dependencies to pubspec.yaml Understanding the mapping structure Placing the images of the actual pieces Making the pieces movable Integrating the chess engine API with a UI Creating the material app Summary Chapter 09: Building an Image Super-Resolution Application Basic project architecture Understanding GANs Understanding how image super-resolution works Understanding image resolution Pixel resolution Spatial resolution Temporal resolution Spectral resolution Radiometric resolution Understanding SRGANs Creating a TensorFlow model for super-resolution Project directory structure Creating an SRGAN model for super-resolution Building the UI for the application Getting pictures from the device's local storage Hosting a TensorFlow model on DigitalOcean Creating a Flask server script Deploying the Flask script to DigitalOcean Droplet Integrating a hosted custom model on Flutter Creating the Material app Summary Chapter 10: Road Ahead Understanding recent trends in DL on mobile applications Math solver Netflix Google Maps Tinder Snapchat Exploring the latest developments in DL on mobile devices Google's MobileNet Alibaba Mobile Neural Network Exploring current research areas for DL in mobile apps Fashion images Self-Attention Generative Adversarial Networks Image animation Summary Appendix Other Books You May Enjoy Index