Book Image

Mobile Artificial Intelligence Projects

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

Mobile Artificial Intelligence Projects

By: Karthikeyan NG, Arun Padmanabhan, Matt 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)
6
PyTorch Experiments on NLP and RNN
7
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
8
Implementing GANs to Recognize Handwritten Digits

Implementing GANs to Recognize Handwritten Digits

In this chapter, we will build an Android application that detects handwritten numbers and works out what the number is by using adversarial learning. We will use the Modified National Institute of Standards and Technology (MNIST) dataset for digit classification. We will also look into the basics of Generative Adversarial Networks (GANs).

In this chapter, we will take a closer look at the following topics:

  • Introduction to GANs
  • Understanding the MNIST database
  • Building the TensorFlow model
  • Building the Android application
The code for this application can be found at https://github.com/intrepidkarthi/AImobileapps.