1 Introduction to Natural Language Processing
This chapter covers
- What NLP is, what NLP is not, and why it’s such an interesting yet challenging field
- How NLP relates to other fields, including Artificial Intelligence (AI) and Machine Learning (ML)
- What typical NLP applications and tasks are
- How a typical NLP application is developed and structured
This is not an introductory book to machine learning or deep learning. You won’t learn how to write neural networks in mathematical terms or how to perform backpropagation, for example. But don’t worry even if you don’t have any idea what they are. I’ll explain those concepts as needed, not mathematically but conceptually. In fact, there will be no mathematical formulae in this book. Not a single one. Also, thanks to modern deep learning libraries, you don’t really need to understand the math in order to build practical NLP applications. If you are interested in learning the theories and the math behind machine learning and deep learning, there are a number of great resources out there.
But at least you need to be comfortable enough to write in Python and know its ecosystems. However, you don’t need to be an expert in software engineering topics. In fact, it is this book’s purpose to introduce software engineering best practices in developing NLP applications. You also don’t need to know NLP in advance. Again, this book is designed to be a gentle introduction to the filed.