Book Image

Machine Learning with Core ML

Book Image

Machine Learning with Core ML

Overview of this book

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
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Assisted drawing 


In this section, we will briefly describe this chapter's project and what we aim to achieve. Recall from the previous chapter that we described an application capable of predicting what the user was trying to sketch, and fetched similar images based on the predicted categories, such as a sailboat. Based on this prediction, the application would search and download images of that category. After downloading, it would sort them based on their similarity with regards to the user's sketch. Then it would present the ordered alternatives to the user, which they could swap their sketch with.

The finished project is shown as follows: 

The model used for performing this classification was based on a Convolutional Neural Network (CNN), a type of neural network well suited for understanding images owing to its ability to find local patterns and build on top of these lower patterns to find more complex and interesting patterns. We took advantage of these higher order patterns by using...