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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Artificial Intelligence Concepts and Fundamentals
Artificial Intelligence Concepts and Fundamentals
AI versus machine learning versus deep learning
Evolution of AI
The mechanics behind ANNs
TensorFlow Playground
Summary
Further reading
2
Creating a Real-Estate Price Prediction Mobile App
Creating a Real-Estate Price Prediction Mobile App
Setting up the artificial intelligence environment
Building an ANN model for prediction using Keras and TensorFlow
Serving the model as an API
Creating an Android app to predict house prices
Creating an iOS app to predict house prices
Summary
3
Implementing Deep Net Architectures to Recognize Handwritten Digits
Implementing Deep Net Architectures to Recognize Handwritten Digits
Building a feedforward neural network to recognize handwritten digits, version one
Building a feedforward neural network to recognize handwritten digits, version two
Building a deeper neural network
Introduction to Computer Vision
Summary
Further reading
4
Building a Machine Vision Mobile App to Classify Flower Species
Building a Machine Vision Mobile App to Classify Flower Species
CoreML versus TensorFlow Lite
Datasets for image classification
Summary
5
Building an ML Model to Predict Car Damage Using TensorFlow
Building an ML Model to Predict Car Damage Using TensorFlow
Transfer learning basics
Image dataset collection
Setting up a web application
Summary
6
PyTorch Experiments on NLP and RNN
PyTorch Experiments on NLP and RNN
PyTorch
Summary
7
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
WaveNet
Summary
8
Implementing GANs to Recognize Handwritten Digits
Implementing GANs to Recognize Handwritten Digits
Introduction to GANs
Summary
9
Sentiment Analysis over Text Using LinearSVC
Sentiment Analysis over Text Using LinearSVC
Building the ML model using scikit–learn
Building the iOS application
Summary
10
What is Next?
What is Next?
Popular ML–based cloud services
Building your first ML model
Summary
Further reading
11
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Building the ML model using scikit–learn

In this section, we will build our own model. There are existing datasets available that are related to Twitter feed data on the topic of product and movie reviews. You can pick a dataset that suits you; in this chapter, we will pick a dataset that has customer reviews.

A dataset that contains both positive and negative reviews of customers can be found at http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/. You can download the dataset from the following link: http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/epinions3.zip.

The aforementioned dataset has both positive and negative feedback about a product, as shown in the following screenshot:

We will train the dataset using the scikit-learn pipeline and LinearSVC. Let's take a closer look at both of these.

...
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