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

Sentiment Analysis over Text Using LinearSVC

In this chapter, we are going to build an iOS application to do sentiment analysis over text and image through user input. We will use existing data models that were built for the same purpose by using LinearSVC, and convert those models into core machine learning (ML) models for ease of use in our application.

Sentiment analysis is the process of identifying a feeling or opinion that is inspired by any given data in the form of text, image, audio, or video. There are a lot of use cases for sentiment analysis. Even now, political parties can easily identify the general mindset of the people who are going to elect them and they also have the potential to change that mindset.

Let's take a look at building our own ML model on sentiment analysis from an existing dataset. In this chapter, we will look at the following topics:

  • Building...