Mobile Artificial Intelligence Projects

By : NG, Padmanabhan, Matt Cole

5 (1)

Buy this Book

Mobile Artificial Intelligence Projects

5 (1)

By: NG, Padmanabhan, Matt Cole

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Artificial Intelligence Concepts and Fundamentals

AI versus machine learning versus deep learning

Evolution of AI

The mechanics behind ANNs

TensorFlow Playground

Summary

Further reading

Building a Machine Vision Mobile App to Classify Flower Species

CoreML versus TensorFlow Lite

Datasets for image classification

Summary

Building an ML Model to Predict Car Damage Using TensorFlow

Transfer learning basics

Image dataset collection

Setting up a web application

Summary

PyTorch Experiments on NLP and RNN

PyTorch

Summary

TensorFlow on Mobile with Speech-to-Text with the WaveNet Model

WaveNet

Summary

Implementing GANs to Recognize Handwritten Digits

Introduction to GANs

Summary

Sentiment Analysis over Text Using LinearSVC

Building the ML model using scikit–learn

Building the iOS application

Summary

What is Next?

Popular ML–based cloud services

Building your first ML model

Summary

Implementing Deep Net Architectures to Recognize Handwritten Digits

In the previous chapters, we have been through the essential concepts and have set up tools that are required for us to get our journey into Artificial Intelligence (AI) started. We also built a small prediction app to get our feet wet with the tools we will be using.

In this chapter, we are going to cover a more interesting and popular application of AI – Computer Vision, or Machine Vision. We will start by continuing from the previous chapter and ease into building convolutional neural networks (CNN), the most popular neural network type for Computer Vision. This chapter will also cover the essential concepts that were promised in Chapter 1, Artificial Intelligence Concepts and Fundamentals, but, in contrast, this chapter will have a very hands-on approach.

We will be covering the following topics in...