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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

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

Further reading

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

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

Further reading

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

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

PyTorch Experiments on NLP and RNN

PyTorch Experiments on NLP and RNN

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

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

Implementing GANs to Recognize Handwritten Digits

Implementing GANs to Recognize Handwritten Digits

Introduction to GANs

Sentiment Analysis over Text Using LinearSVC

Sentiment Analysis over Text Using LinearSVC

Building the ML model using scikit–learn

Building the iOS application

What is Next?

Popular ML–based cloud services

Building your first ML model

Further reading

Other Books You May Enjoy

Other Books You May Enjoy

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