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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 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

Image dataset collection

For our experiment, we need the datasets for cars in good condition as well as in damaged condition. If you have a data source that adheres to the privacy policy, then this is a good place to start. Otherwise, we need to find a way to build our model on top of a dataset. There are multiple datasets that are publicly available. We need to start building our dataset if there is no existing reference of a similar data model because this could be a time-consuming task as well as an important step toward getting better results.

We are going to use a simple Python script to download images from Google. Just make sure that you filter images that can be reused. We don't encourage using pictures with non-reusable licenses.

With the Python script, we will pull out and save the images from Google, and then we will use a library to do the same task. This step...

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