Book Image

Mobile Artificial Intelligence Projects

By : Karthikeyan NG, Arun Padmanabhan, Matt R. Cole
Book Image

Mobile Artificial Intelligence Projects

By: Karthikeyan NG, Arun Padmanabhan, Matt R. Cole

Overview of this book

We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.
Table of Contents (12 chapters)
PyTorch Experiments on NLP and RNN
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
Implementing GANs to Recognize Handwritten Digits

CoreML versus TensorFlow Lite

In the machine learning world, there are two efforts (as of the time of this writing) taking place in order to improve the mobile AI experience. Instead of offloading AI or ML processing to the cloud and a data center, the faster option would be to process data on the device itself. In order to do this, the model must already be pre-trained, which means that there is a chance that it is not trained for exactly what you are going to use it for.

In this space, Apple’s effort (iOS) is called Core ML, and Google’s (Android) is called TensorFlow Lite. Let’s talk briefly about both.


The CoreML framework from Apple provides a large selection of neural network types. This allows...