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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

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...
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