Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Introducing Core ML

Core ML was first presented at Apple WWDC 2017. Defining Core ML as machine learning framework is not fair, because it lacks learning capabilities; it's rather a set of conversion scripts to plug the pre-trained model into your Apple applications. Still, it is an easy way for newcomers to start running their first models on iOS.

Core ML features

Here is a list of Core ML features:

  • coremltools Python package includes several converters for popular machine learning frameworks: scikit-learn, Keras, Caffe, LIBSVM, and XGBoost.
  • Core ML framework allows running inference (making predictions) on a device. Scikit-learn converter also supports some data transformation and model pipelining.
  • Hardware acceleration (Accelerate framework and Metal under the hood).
  • Supports iOS, macOS, tvOS, and watchOS.
  • Automatic code generation for OOP-style interoperability with Swift.

The biggest Core ML limitation is that it doesn't support models training.

Exporting the model for iOS

In our Jupyter notebook...