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)

Speech/Multimedia Processing - Generating Music Using AI

Given the increasing number of applications of artificial intelligence (AI), the idea of using AI with music has been around for a long time and is much researched. Since music is a series of notes, it is a classic example of a time series dataset. Time series datasets have proven very useful recently in a number of forecast areas – stock markets, weather patterns, sales patterns, and other time-based datasets. Recurrent neural networks (RNNs) are one of the most models for working with time series datasets. A popular enhancement made to RNNs is called long short-term memory (LSTM) neurons. We'll be using LSTMs in this chapter to work with the music notes.

Multimedia processing, too, isn't a new topic. Earlier in this project series, we covered image processing in detail in multiple chapters. In this...