Book Details
Develop Intelligent iOS Apps with Swift
Understand Texts, Classify Sentiments, and Autodetect Answers in Text Using NLP
Acknowledgments
I’d like to take this opportunity to gratefully thank the people who have contributed toward the development of this book:
Aaron Black, Senior Editor at Apress, who saw potential in the idea behind the book. He helped kick-start the book with his intuitive suggestions.
James Markham, Development Editor at Apress, who made sure that the content quality of the book remains uncompromised.
Jessica Vakili, Coordinating Editor at Apress, who made sure that the process from penning to publishing the book remained smooth and hassle-free.
Mom, Dad, and my love, Evrim, all of whom were nothing but supportive while I was writing this book. They have always been there for me, encouraging me to achieve my aspirations.
Countless number of iOS developers who share their knowledge with the community.
I hope many developers find this book guiding through their first steps to mobile machine learning (ML). You encourage me to learn more and share.
Thanks!
CHAPTER 1
A Gentle Introduction to ML and NLP
This chapter will provide you a bird’s-eye view of machine learning (ML) and deep learning (DL). The history of these fields will be storified here in order to be more understandable. We will examine why they have emerged and what kind of applications they have. After gaining the principal knowledge, you will be introduced to natural language processing (NLP). You will learn how we make text data understandable for computers via NLP. Even if you have zero knowledge about these disciplines, you will gain the intuition behind after reading this chapter.
What Is Machine Learning?
As Homo sapiens, we like to create tools that will save us time and energy. First, humans started to use animals to be freed of manpower. With the industrial revolution, we started to use machines instead of the human body. The current focus of humanity is to transfer thinking and learning skills to machines to get rid of mundane mental tasks. The improvement
of this field in the last decades is very significant. We don’t have general AI
yet that can do any intellectual task, but we have built successful AI models
that can do specific tasks very well like understanding human language
or finding the answer to a question in an article. In some tasks like image classification, it is even better than humans.
Machine learning is a buzzword nowadays. There are plenty of theories
going around, but it’s hard to see real applications that can be built by
an indie developer. Developing an end-to-end machine learning system
requires a wide range of expertise in areas like linear algebra, vector
calculus, statistics, and optimization.
Therefore, from a developer’s perspective, there’s a high learning curve that stands in the way, but the latest tools take care of most of the work for developers, leaving them free to code. In this book, you will learn how to build machine learning applications that can extract text from an image (OCR), classify text, find answers in an article, summarize text, and generate sentences when given an input sentence. You will be armed with cutting-edge tools offered by Apple and able to develop your smart apps. We will learn by coding; some of the apps we will develop will look like those in Figure
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