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Mobile Artificial Intelligence Projects

You're reading from   Mobile Artificial Intelligence Projects Develop seven projects on your smartphone using artificial intelligence and deep learning techniques

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Length 312 pages
Edition 1st Edition
Languages
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Authors (3):
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Arun Padmanabhan Arun Padmanabhan
Author Profile Icon Arun Padmanabhan
Arun Padmanabhan
Karthikeyan NG Karthikeyan NG
Author Profile Icon Karthikeyan NG
Karthikeyan NG
Matt Cole Matt Cole
Author Profile Icon Matt Cole
Matt Cole
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Toc

Table of Contents (12) Chapters Close

Preface 1. Artificial Intelligence Concepts and Fundamentals FREE CHAPTER 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

To get the most out of this book

Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.

Download the example code files

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  1. Log in or register at www.packt.com.
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  4. Enter the name of the book in the Search box and follow the onscreen instructions.

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mobile-Artificial-Intelligence-Projects. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

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Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "First, let's import the math library so that we can use the exponential function."

A block of code is set as follows:

def sigmoid ( x ):    
return
1 / ( 1 + e **- x )

Any command-line input or output is written as follows:

pip install tensorflow

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Use the New dropdown in the top-right corner to create a new Python 3 notebook."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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