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Mobile Artificial Intelligence Projects
Mobile Artificial Intelligence Projects

Mobile Artificial Intelligence Projects: Develop seven projects on your smartphone using artificial intelligence and deep learning techniques

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Profile Icon NG Profile Icon Padmanabhan Profile Icon Matt Cole
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Paperback Mar 2019 312 pages 1st Edition
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Arrow left icon
Profile Icon NG Profile Icon Padmanabhan Profile Icon Matt Cole
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Mar 2019 312 pages 1st Edition
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$17.99 $26.99
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Mobile Artificial Intelligence Projects

Creating a Real-Estate Price Prediction Mobile App

In the previous chapter, we covered the theoretical fundamentals; this chapter, on the other hand, will cover the setup of all the tools and libraries.

First, we are going to set up our environment to build a Keras model to predict house prices with real estate data. Then we are going to serve this model using a RESTful API built using Flask. Next, we will set up our environment for Android and create an app that will consume this RESTful API to predict the house price based on features of real estate. Finally, we will repeat the same exercise for iOS.

The focus of this chapter is on the setup, tools, libraries, and exercising the concepts learned in Chapter 1, Artificial Intelligence Concepts and Fundamentals. The use case is designed to be simple, yet adaptable enough to accommodate similar use-cases. By the end of the chapter...

Setting up the artificial intelligence environment

The first thing to do is install Python. We are going to use Python throughout this book for all our artificial intelligence (AI) tasks. There are two ways to install Python, either through the downloadable executable file provided from https://www.python.org/downloads/ or via Anaconda. Our approach will be the latter, that is, using Anaconda.

Downloading and installing Anaconda

Building an ANN model for prediction using Keras and TensorFlow

Now that we have our libraries installed, let's create a folder called aibook and within that create another folder called chapter2. Move all the code for this chapter into the chapter2 folder. Make sure that the conda environment is still active (the prompt will start with the environment name):

Once within the chapter2 folder, type jupyter notebook. This will open an interactive Python editor on the browser.

Use the New dropdown in the top-right corner to create a new Python 3 notebook:

We are now ready to build our first ANN using Keras and TensorFlow, to predict real estate prices:

  1. Import all the libraries that we need for this exercise. Use the first cell to import all the libraries and run it. Here are the four main libraries we will use:
    • pandas: We use this to read the data and store it in a dataframe...

Serving the model as an API

Now that we have created a model for prediction, the next thing is to serve this model via a RESTful API. To achieve this, we will use lightweight Python framework called Flask: http://flask.pocoo.org/.

Let's start by installing the Flask library in our conda environment if it does not already exist:

pip install Flask

Building a simple API to add two numbers

Now we will build a very simple API to get a grip on the Flask library and framework. This API will accept a JSON object with two numbers and return the sum of the numbers as a response.

Open a new notebook from your Jupyter home page:

  1. Import all the libraries we need and create an app instance:
from flask import Flask, request 
app =...

Creating an Android app to predict house prices

In this section, we are going to consume the model through the RESTful API via an Android app. The purpose of this section is to demonstrate how a model can be consumed and used by an Android app. Here, we have assumed that you are familiar with the basics of Java programming. The same approach can be used for any similar use case, even on web apps. The following steps are covered in this section:

  • Downloading and installing Android Studio
  • Creating a new Android project with a single screen
  • Designing the layout of the screen
  • Adding a functionality to accept input
  • Adding a functionality to consume the RESTful API that serves the model
  • Additional notes

Downloading and installing Android Studio

...

Creating an iOS app to predict house prices

In this section, we are going to consume the model through the RESTful API via an iOS app. The purpose of this section is to demonstrate how a model can be consumed and used by an iOS app. Here, we have assumed that you are familiar with Swift programming. The same approach can be used for any similar use case. These are the following steps covered in this section:

  • Downloading and installing Xcode
  • Creating a new iOS project with a single screen
  • Designing the layout of the screen
  • Adding a functionality to accept input
  • Adding a functionality to consume the RESTful API that serves the model
  • Additional notes

Downloading and installing Xcode

You need a Mac (macOS 10.11.5 or later) to...

Summary

In this chapter, we explored the basic sequential network and consumed it on mobile devices. In the next chapter, we will take a look at a special kind of network called Convolutional Neural Networks (CNN). CNNs are the most common networks used with Machine Vision. Our goal in the next chapter is to get comfortable with machine vision and to build our own custom-purpose CNNs.

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Key benefits

  • 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

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.

Who is this book 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.

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

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Publication date : Mar 30, 2019
Length: 312 pages
Edition : 1st
Language : English
ISBN-13 : 9781789344073
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Product Details

Publication date : Mar 30, 2019
Length: 312 pages
Edition : 1st
Language : English
ISBN-13 : 9781789344073
Vendor :
Google
Category :
Languages :

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Table of Contents

11 Chapters
Artificial Intelligence Concepts and Fundamentals Chevron down icon Chevron up icon
Creating a Real-Estate Price Prediction Mobile App Chevron down icon Chevron up icon
Implementing Deep Net Architectures to Recognize Handwritten Digits Chevron down icon Chevron up icon
Building a Machine Vision Mobile App to Classify Flower Species Chevron down icon Chevron up icon
Building an ML Model to Predict Car Damage Using TensorFlow Chevron down icon Chevron up icon
PyTorch Experiments on NLP and RNN Chevron down icon Chevron up icon
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model Chevron down icon Chevron up icon
Implementing GANs to Recognize Handwritten Digits Chevron down icon Chevron up icon
Sentiment Analysis over Text Using LinearSVC Chevron down icon Chevron up icon
What is Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Good book to learn about deeplearning for beginners
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