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

Choosing a deep learning framework

Choosing the correct deep learning framework is important to get the optimal speed and model size you desire. There are several things to consider—overhead, added by the library, GPU acceleration, do you need training or inference only?, in which framework existing solutions were implemented.

You should understand that you don't always need GPU acceleration. Sometimes, SIMD/Accelerate is more than enough to implement neural networks that do inference in real-time.

Sometimes, you have to consider whether the calculations are going to be done on the client side, on the server side, or if they will be balanced between both. Try to do benchmarks with an extreme number of records, and test them with different devices.