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 GANs

GANs, which were introduced by Ian Goodfellow, Yoshua Bengio, and others in NeurIPS 2014, took the world by storm. GANs, which can be applied to all sorts of domains, generate new content or sequences based on the model's learned approximation of real-world data samples. GANs have been used heavily for generating new samples of music and art, such as the faces shown in the following image, none of which existed in the training dataset:

Faces generated by GAN after 60 epochs of training. This image has been taken from

The amount of realism that's present in the preceding faces demonstrates the power of GANs – they can pretty much learn to generate any sort of pattern when they've been given a good training sample size. 

The core concept of GANs revolves around the idea of two players...