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)

Understanding an image caption generator

A very popular domain of computer science is that of image processing. It deals with the manipulation of images and the various methods by which we can extract information from them. Another popular domain, Natural Language Processing (NLP), deals with how we can make machines that can understand and produce meaningful natural languages. Image captioning defines a mixture of the two topics, which attempts to first extract the information of objects appearing in any image and then to generate a caption describing the objects.

The caption should be generated in such a way that it is a meaningful string of words and is expressed in the form of a natural language sentence. 

Consider the following image:

The objects that can be detected in the image are as follows: spoon, glass, coffee, and table.

However, do we have answers to the following...