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

Building a Machine Vision Mobile App to Classify Flower Species

In this chapter, we are going to use the theoretical knowledge we have learned in previous chapters to create a mobile application that will classify a specific species of flower. By utilizing use your mobile camera and pointing it at a flower, the application will analyze the image and make its best educated guess as to the species of that flower. This is where we put to work the understanding we have gained about the workings of a convolutional neural network (CNN). We will also learn a bit more about using TensorFlow as well as some tools such as TensorBoard. But before we dive in too deep, let’s talk about a few things first.

Throughout this chapter we use terms that may not be familiar to all, so let’s make sure we’re all on the same page as to what they mean.

In this chapter, we will cover...