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

NLP in the mobile development world

Usually, NLP specialists deal with big amounts of raw text organized in linguistics corpuses. The algorithms in this domain are resource-consuming and often contain many hand-crafted heuristics. All this doesn't look like a good match for mobile applications, where each megabyte or frame per second is important. Despite these obstacles, NLP is widely used on mobile platforms, usually in tight integration with the server-side backend for heavy computations. Here is a list of some common NLP features that can be found in many mobile applications:

  • Chatbots
  • Spam filtering
  • Automated translation
  • Sentiment analysis
  • Speech-to-text and text-to-speech
  • Automatic spelling and grammar correction
  • Automatic completion
  • Keyboard suggestions

Until recently, all but the last two tasks were done on the server side, but as mobile computational power grows, more apps tend to do processing (at least partially) locally on the client. When we talk about NLP on a mobile device, in most...