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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

CoreML versus TensorFlow Lite

In the machine learning world, there are two efforts (as of the time of this writing) taking place in order to improve the mobile AI experience. Instead of offloading AI or ML processing to the cloud and a data center, the faster option would be to process data on the device itself. In order to do this, the model must already be pre-trained, which means that there is a chance that it is not trained for exactly what you are going to use it for.

In this space, Apple’s effort (iOS) is called Core ML, and Google’s (Android) is called TensorFlow Lite. Let’s talk briefly about both.

CoreML

The CoreML framework from Apple provides a large selection of neural network types. This allows...

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