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Machine Learning with Swift

You're reading from  Machine Learning with Swift

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Pages 378 pages
Edition 1st Edition
Languages
Authors (3):
Jojo Moolayil Jojo Moolayil
Profile icon Jojo Moolayil
Alexander Sosnovshchenko Alexander Sosnovshchenko
Profile icon Alexander Sosnovshchenko
Oleksandr Baiev Oleksandr Baiev
View More author details

Table of Contents (18) Chapters

Title Page
Packt Upsell
Contributors
Preface
Getting Started with Machine Learning Classification – Decision Tree Learning K-Nearest Neighbors Classifier K-Means Clustering Association Rule Learning Linear Regression and Gradient Descent Linear Classifier and Logistic Regression Neural Networks Convolutional Neural Networks Natural Language Processing Machine Learning Libraries Optimizing Neural Networks for Mobile Devices Best Practices Index

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...

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