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

Facial expressions


Our face is one of the strongest indicators of emotions; as we laugh or cry, we put our emotions on display, allowing others to glimpse into our minds. It's a form of nonverbal communication that, apparently, accounts for over 50% of our communication with others. Forty independently controlled muscles make the face one of the most complex systems we possess, which could be the reason we use it as a medium for communicating something so important as our current emotional state. But can we classify it?

In 2013, the International Conference on Machine Learning (ICML) ran a competition inviting contestants to build a facial expression classifier using a training dataset of over 28,000 grayscale images. They were labeled as either anger, disgust, fear, happiness, sadness, surprise, or neutral. The following are a few samples of this training data (available at https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge):

As previously...