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
Contributors
Preface
Index

What is AI?


"What I cannot create, I do not understand."

– Richard Feynman

AI is a field of knowledge about building intelligent machines, whatever meaning you assign to the word intelligence. There are two different AI notions among researchers: strong AI and weak AI.

Strong AI, or artificial general intelligence (AGI), is a machine that is fully capable of imitating human-level intelligence, including consciousness, feelings, and mind. Presumably, it should be able to apply successfully its intelligence to any tasks. This type of AI is like a horizon—we always see it as a goal but we are still not there, despite all our struggles. The significant role here plays the AI effect: the things that were yesterday considered a feature of strong AI are today accepted as granted and trivial. In the sixties, people believed that playing board games like chess was a characteristic of strong AI. Today, we have programs that outperform the best human chess players, but we are still far from strong AI. Our iPhones are probably an AI from the eighties perspective: you can talk to them, and they can answer your questions and deliver information on any topic in just seconds. So, keeping strong AI as a distant goal, researchers focused on things at hand and called them weak AI: systems that have some features of intelligence, and can be applied to some narrow tasks. Among those tasks are automated reasoning, planning, creativity, communication with humans, a perception of its surrounding world, robotics, and emotions simulation. We will touch some of these tasks in this book, but mostly we will focus on ML because this domain of AI has found a lot of practical applications on mobile platforms in the recent years.