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Machine Learning for Mobile

You're reading from  Machine Learning for Mobile

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788629355
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Revathi Gopalakrishnan Revathi Gopalakrishnan
Profile icon Revathi Gopalakrishnan
Avinash Venkateswarlu Avinash Venkateswarlu
Profile icon Avinash Venkateswarlu
View More author details

Table of Contents (19) Chapters

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Introduction to Machine Learning on Mobile 2. Supervised and Unsupervised Learning Algorithms 3. Random Forest on iOS 4. TensorFlow Mobile in Android 5. Regression Using Core ML in iOS 6. The ML Kit SDK 7. Spam Message Detection 8. Fritz 9. Neural Networks on Mobile 10. Mobile Application Using Google Vision 11. The Future of ML on Mobile Applications 1. Question and Answers 2. Other Books You May Enjoy Index

Chapter 10. Mobile Application Using Google Vision

As we saw in Chapter 1, Introduction to Machine Learning on Mobile, we know that machine learning in mobile applications can be implemented either on-device or it can be implemented using machine learning cloud provider services. There are various machine learning cloud providers:

  • Clarifai
  • Google Cloud Vision
  • Microsoft Azure Cognitive Services
  • IBM Watson
  • Amazon Machine Learning

In this chapter, we are going to dive deeply into Google Cloud Vision to understand the following:

  • Features of Google Cloud Vision
  • How to utilize the Google Cloud Vision label-detection technique in an Android Mobile application to determine what is the picture taken by the camera. That is, we basically feed an image into Google Cloud Vision and see how it labels the image. Google Vision is going to predict the image that it receives from the mobile application and provide a label for the image. 
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