Mobile Artificial Intelligence Projects

Develop seven projects on your smartphone using artificial intelligence and deep learning techniques

Karthikeyan NG, Arun Padmanabhan, Matt R. Cole

• Publisher: Packt Publishing
• Year: 2019
• Print ISBN: 9781789344073
• eBook ISBN: 9781789347043
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch

Key Features

• Build practical, real-world AI projects on Android and iOS
• Implement tasks such as recognizing handwritten digits, sentiment analysis, and more
• Explore the core functions of machine learning, deep learning, and mobile vision

Book Description

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.

What you will learn

• Explore the concepts and fundamentals of AI, deep learning, and neural networks
• Implement use cases for machine vision and natural language processing
• Build an ML model to predict car damage using TensorFlow
• Deploy TensorFlow on mobile to convert speech to text
• Implement GAN to recognize hand-written digits
• Develop end-to-end mobile applications that use AI principles
• Work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch

Who this book is for

Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.

Artificial Intelligence Concepts and Fundamentals

This chapter acts as a prelude to the entire book and the concepts within it. We will understand these concepts at a level high enough for us to appreciate what we will be building throughout the book.

We will start by getting our head around the general structure of Artificial Intelligence (AI) and its building blocks by comparing AI, machine learning, and deep learning, as these terms can be used interchangeably. Then, we will skim through the history, evolution, and principles behind Artificial Neural Networks (ANNs). Later, we will dive into the fundamental concepts and terms of ANNs and deep learning that will be used throughout the book. After that, we take a brief look at the TensorFlow Playground to reinforce our understanding of ANNs. Finally, we will finish off the chapter with thoughts on where to get a deeper theoretical reference for the high-level concepts of the AI and ANN principles covered in this chapter, which will be as follows:

• AI versus machine learning versus deep learning
• Evolution of AI
• The mechanics behind ANNs
• Biological neurons
• Working of artificial neurons
• Activation and cost functions
• Gradient descent, backpropagation, and softmax
• TensorFlow Playground

AI versus machine learning versus deep learning

AI is no new term given the plethora of articles we read online and the many movies based on it. So, before we proceed any further, let's take a step back and understand AI and the terms that regularly accompany it from a practitioner's point of view. We will get a clear distinction of what machine learning, deep learning, and AI are, as these terms are often used interchangeably:

AI is the capability that can be embedded into machines that allows machines to perform tasks that are characteristic of human intelligence. These tasks include seeing and recognizing objects, listening and distinguishing sounds, understanding and comprehending language, and other similar tasks.

Machine learning (ML) is a subset of AI that encompasses techniques used to make these human-like tasks possible. So, in a way, ML is what is used to achieve AI.

In essence, if we did not use ML to achieve these tasks, then we would actually be trying to write millions of lines of code with complex loops, rules, and decision trees.

ML gives machines the ability to learn without being explicitly programmed. So, instead of hardcoding rules for every possible scenario to a task, we simply provide examples of how the task is done versus how it should not be done. ML then trains the system on this provided data so it can learn for itself.

ML is an approach to AI where we can achieve tasks such as grouping or clustering, classifying, recommending, predicting, and forecasting data. Some common examples of this are classifying spam mail, stock market predictions, weather forecasting, and more.

Deep learning is a special technique in ML that emulates the human brain's biological structure and works to accomplish human-like tasks. This is done by building a network of neurons just like in the brain through an algorithmic approach using ANNs, which are stack of algorithms that can solve problems at human-like efficiency or better.

These layers are commonly referenced as deepnets (deep architectures) and each has a specific problem that it can be trained to solve. The deep learning space is currently at the cutting edge of what we see today, with applications such as autonomous driving, Alexa and Siri, machine vision, and more.

Throughout this book, we will be executing tasks and building apps that are built using these deepnets, and we will also solve use cases by building our very own deepnet architecture.

Evolution of AI

To appreciate what we can currently do with AI, we need to get a basic understanding of how the idea of emulating the human brain was born, and how this idea evolved to a point where we can easily solve tasks in vision and language with human-like cap...

Table of contents

• Title Page
• Copyright and Credits
• Dedication
• About Packt
• Contributors
• Preface
• Artificial Intelligence Concepts and Fundamentals
• Creating a Real-Estate Price Prediction Mobile App
• Implementing Deep Net Architectures to Recognize Handwritten Digits
• Building a Machine Vision Mobile App to Classify Flower Species
• Building an ML Model to Predict Car Damage Using TensorFlow
• 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
• What is Next?
• Other Books You May Enjoy