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Machine Learning with Core ML

You're reading from   Machine Learning with Core ML An iOS developer's guide to implementing machine learning in mobile apps

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838290
Length 378 pages
Edition 1st Edition
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Author (1):
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Joshua Newnham Joshua Newnham
Author Profile Icon Joshua Newnham
Joshua Newnham
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Introduction to Apple Core ML 3. Recognizing Objects in the World 4. Emotion Detection with CNNs 5. Locating Objects in the World 6. Creating Art with Style Transfer 7. Assisted Drawing with CNNs 8. Assisted Drawing with RNNs 9. Object Segmentation Using CNNs 10. An Introduction to Create ML 11. Other Books You May Enjoy

Considerations 

When performing ML on the edge, you lose some of the luxuries you tend to have when running on a more powerful device (albeit this is shifting all the time). Here is a list of considerations to keep in mind:

  • Model size: Previously, we walked through building a simple linear regression model. The model itself consists of two floats (bias and weight coefficients), which of course are negligible in terms of memory requirements. But, as you dive into the world of deep learning, it's common to find models hundreds of megabytes in size. For example, the VGG16 model is a 16-layer conventional neural network architecture trained on the ImageNet dataset used for image classification, available on Apple's site. It is just over 500 megabytes. Currently, Apple allows apps 2 gigabytes in size, but asking your user to download such a large file...
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