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

Recommended learning resources

In this book, we've only scratched the surface of the immense body of knowledge behind the term machine learning. If you want to learn more, we highly recommend the following resources.

The main criteria for choosing the courses and books were clarity of presentation and a CS-oriented approach. Other criteria for the books were free online availability and open source code samples. All courses mentioned in this list are free (as of May 2017) and of introductory level.

Mathematical background

The handwritten comic-style lectures on Calculus by Robert Ghrist from the University of Pennsylvania can be found on YouTube or Coursera. This teaches single-variable calculus: Taylor series, Newton method. This should be your choice if you don't know how to take a derivative of a sigmoid function or which functions are differentiable. For more information refer to:

Coding The Matrix: Linear Algebra Through Computer Science Applications...