Baiev Oleksandr - Machine Learning with Swift: Artificial Intelligence for iOS

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First published: February 2018

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Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.

Jojo Moolayil is an artificial intelligence, deep learning, and machine learning professional with over 5 years of experience and is the author of Smarter Decisions The Intersection of Internet of Things and Decision Science. He works with GE and lives in Bengaluru, India. He has also been a technical reviewer about various books in machine learning, deep learning, and business analytics with Apress and Packt.

Cecil Costa, also known as Eduardo Campos in Latin American countries, is a Euro-Brazilian freelance developer who has been learning about computers since he got his first PC in 1990. Learning is his passion, and so is teaching; this is why he works as a trainer. He has organized both on-site and online courses for companies. He is also the author of a few Swift books.

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Machine learning, as a field, promises to bring increasing intelligence to software by helping us learn and analyze information efficiently and discover certain things that humans cannot. We'll start by developing lasting intuition about the fundamental machine learning concepts in the first section. We'll explore various supervised and unsupervised learning techniques in the second section. Then, the third section, will walk you through deep learning techniques with the help of common real-world cases.
In the last section, we'll dive into hardcore topics such as model compression and 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.

This book is for iOS developers who wish to create intelligent iOS applications, and data science professionals who are interested in performing machine learning using Swift. Familiarity with some basic Swift programming is all you need to get started with this book.

, Getting Started with Machine Learning , teaches the main concepts of machine learning.

, Classification Decision Tree Learning , builds our first machine learning application.

, K-Nearest Neighbors Classifier , continues exploring classification algorithms, and we learn about instance-based learning algorithms.

, K-Means Clustering , continues with instance-based algorithms, this time focusing on an unsupervised clustering task.

, Association Rule Learning , explores unsupervised learning more deeply.

, Linear Regression and Gradient Descent