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 feedforward neural network to recognize handwritten digits, version one

In this section, we will use the knowledge that we gained from the last two chapters to tackle a problem that has unstructured data – image classification. The idea is to take a dive into solving a Computer Vision task with the current setup and the basics of neural networks that we are familiar with. We have seen that feedforward neural networks can be used for prediction using structured data; let's try that on images to classify handwritten digits.

To solve this task, we are going to leverage the MNSIT database and use the handwritten digits dataset. MNSIT stands for Modified National Institute of Standards and Technology. It is a large database that's commonly used for training, testing, and benchmarking image-related tasks in Computer Vision.

The MNSIT digits dataset contains...