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

Image dataset collection

For our experiment, we need the datasets for cars in good condition as well as in damaged condition. If you have a data source that adheres to the privacy policy, then this is a good place to start. Otherwise, we need to find a way to build our model on top of a dataset. There are multiple datasets that are publicly available. We need to start building our dataset if there is no existing reference of a similar data model because this could be a time-consuming task as well as an important step toward getting better results.

We are going to use a simple Python script to download images from Google. Just make sure that you filter images that can be reused. We don't encourage using pictures with non-reusable licenses.

With the Python script, we will pull out and save the images from Google, and then we will use a library to do the same task. This step...