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Machine Learning with Core ML

More Information
Learn
  • Understand components of an ML project using algorithms, problems, and data
  • Master Core ML by obtaining and importing machine learning model, and generate classes
  • Prepare data for machine learning model and interpret results for optimized solutions
  • Create and optimize custom layers for unsupported layers
  • Apply CoreML to image and video data using CNN
  • Learn the qualities of RNN to recognize sketches, and augment drawing
  • Use Core ML transfer learning to execute style transfer on images
About

Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps.

Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this book, you’ll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You’ll learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you’ll discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts.

By the end of the book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs

Features
  • Explore the concepts of machine learning and Apple’s Core ML APIs
  • Use Core ML to understand and transform images and videos
  • Exploit the power of using CNN and RNN in iOS applications
Page Count 378
Course Length 11 hours 20 minutes
ISBN 9781788838290
Date Of Publication 27 Jun 2018
Facial expressions
Input data and preprocessing 
Bringing it all together
Summary 
Towards intelligent interfaces 
Drawing
Recognizing the user's sketch
Summary 
Assisted drawing 
Recurrent Neural Networks for drawing classification
Input data and preprocessing 
Bringing it all together
Summary 
Classifying pixels 
Data to drive the desired effect – action shots
Building the photo effects application
Working with probabilistic results
Summary

Authors

Joshua Newnham

Joshua Newnham is a technology lead at a global design firm, Method, focusing on the intersection of design and artificial intelligence (AI), specifically in the areas of computational design and human computer interaction.

Prior to this, he was a technical director at Masters of Pie, a virtual reality (VR) and augmented reality (AR) studio focused on building collaborative tools for engineers and creatives.