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

Machine learning gremlins

Ben Hamner, a data scientist at Kaggle, referred to common machine learning gotchas as ML gremlins.


You can watch Ben's original talk at:

I like the metaphor because it makes my brain think about evil characters rather than some vague, abstract concepts. In addition to the original gremlins presented by Ben, I want to add several of my own and also present a taxonomy of gremlins (see the following diagram). I employed this metaphor throughout this chapter to avoid boring issues and problems when discussing how to identify and neutralize those pests:

Figure 13.3: The simplified taxonomy of machine learning problems

Data kobolds

Dealing with data is hard; that's why we call it data science and data mining! Many different things can go wrong at different stages. Ben mentions data insufficiency, data leakage, non-stationary distributions, poor data sampling and splitting, data quality, and poorly anonymized data. Let's add...