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
Contributors
Preface
Index

Chapter 13. Best Practices

"The purpose of a storyteller is not to tell you how to think, but to give you questions to think upon."

– Brandon SandersonThe Way of Kings

Imagine the field of AI as a huge national park. In previous chapters, we guided you along several exciting trails and showed you the most interesting sights for mobile developers. But there is still so much more that is unexplored. So, in this chapter, we want to provide you with a map of the common paths, from idea to production. We've outlined dangerous zones and left notes on solo hiking best practices! We also want to point out several interesting directions for your future exploration.

In this chapter, we will discuss the following topics:

  • The path from idea to production
  • Common pitfalls in machine learning projects also known as machine learning gremlins
  • Machine learning best practices
  • Recommended study resources